DG1	Generate a sequence of x values from -5 to +5 with a step of 0.1	x <- seq(-5, 5, 0.1)	Y	c1
DG2	plot them as 2 rows in 1 column	par(mfrow=(c(2,1))		c1
DG3	"How do you make this chart? <img src=""Screenshot_2015-09-13-16-48-16~2.png"" />"	"x <- seq(-5, 5, 0.1)<br>plot(x, dnorm(x, 0, 1.5), type=""l"", main=""Normal PDF"")"		c1
DG4	how to import json data?	"library(jsonlite)<br />json.data &lt;- fromJSON(""nombre del archivo "")"		c1
DG5	Obtain data from URL?	> library (RCurl)<br>> spreadsheet. data <- getURL(URL, follow location=T, SSL.verifypeer=F)<br>		c1
DG6	1) split long character string into individual lines.<br>2) split those lines into individual values.	points <- read.csv(textConnection(spreadsheet.data))		c1
DG7	corrects<br>> mean(ecc$X71)<br>argument is not numeric.	mean(as. numeric(ecc$X71))	y	c1
DG8	I want to aggregate the reltco variable by subsystem, from the data set named temp, using the mean function.	aggregate (reltco~subsystem, data=temp, FUN=mean)		c1
DG9	get first rows of data frame	head (temp)	y	c1
DG10	Show the contents and structure of a  dataframe	<img src='Screenshot_2015-09-13-17-14-33.png'/>		c1







DG11	how to Install and Use descriptive statistics from package?	"install.packages(""pastecs"")<br>library(pastecs)<br><img src='Screenshot_2015-09-13-17-21-02.png'/>"		c1
DG12	What are the meaning of these?<br>nbr.val<br>nbr.null<br>nbr.na<br>range<br>sum	number of observations<br>null observations<br>not available observations<br>difference between max and min values.<br>sum of all values		c1
DG13	Create a new data frame wich consists of all rows from tvs dataframe, and just these 3 columns: depth, top, max	"sub.tvs <- tvs[,c(""depth"",""top"",""max"")]"		c1
DG14	once we have our data in the proper format, we can generate descriptive statistics using the command:	<img src='Screenshot_2015-09-13-17-31-25.png'/>		c1
DG15	this is the formula for?<img src='Screenshot_2015-09-13-17-40-26~2.png'/>	variance with the Bessels correction.		c1
DG16	formula for standard deviation?	"sqrt(var(x))<br /><img src=""Screenshot_2015-09-13-17-43-32.png"" />"		c1
DG17	what is the rationale for Bessel's correction?	variance used to be calculated by multipliying by 1/n.<br>however in a small sample you normally don't capture all the variability that is there.<br>so we need to multiply the variability for a bigger number.<br>which number?<br>well since (1/(n-1)) > (1/n) for example 1/4 > 1/5<br>and this correction will be big for small samples but very small for big samples.<br><br><img src='Screenshot_2015-09-13-17-52-10.png'/>		c1
DG18	generates n random variables selected from a normal distribution with specified mean and standard deviation.	rnorm(n, mean, SD)		c1
DG19	plot the PDF from a collection of values x	dnorm(x, mean, SD)		c1


DG21	Wich function finds the quantiles, or inverse CDF? Supose you know an area under the normal PDF the left of a particular x-value.	qnorm(area, mean, sd)		c1
DG22	PDF illustrates<br />	"how densely are your observations packed around a particular value.<br />when you think of the normal distribution as a bell curve, you're thinking about the PDF.<br /><br><img src=""Screenshot_2015-09-13-16-48-16~2.png"" />"		c1
DG23	CDF <img src='IMG_20150913_164741258~2.jpg'/>	shows what proportion of all observations will be less than or equal to a certain value.	y	c1
DG24	? = CDF	<img src='Screenshot_2015-09-13-18-29-36~2.png'/>		c1
DG25	par(mfrow=c(1,3))	defines a plot area with one row and three columns.	y	c1
DG26	Since pnorm always give us......	the area to the left of a certain point.		c1
DG27	Which are the two ways to use pnorm()?	pnorm(-2) # using the Z-score of a particular point.<br>pnorm(2.99, mean=3, sd=0.005) # using the point and the description of all the curve.		c1
DG28	Quantitative data is to ___________ AS<br>Categorical data is to_____________	HISTOGRAM<br>BAR CHART		c1
DG29	in an HISTOGRAM bars will be...	very cozy, mashed up against one another like they're at a crowded party.<br><br>		c1

DG30	Histogram bars are together whereas in a bar chart the bars need...	a little more breating room.		c1
DG31	unimodal meaning	one hump or most frequent value, wich is called mode	y	c1
DG32	bimodal meaning...	two humps.		c1
DG33	uniform meaning..	all the values have the same probability of being observed in the dataset		c1
DG34	sekewed right meaning...	there's a tail that extends to the right, towards higher values...		c1
DG35	sekewed left	a tail that extends to the left, towards lower values..	y	c1
DG36	symmetric	you can fold the distribution in half, and you'll get mirror images		c1
DG37	asymmetric	you can't fold it to get mirror images.		c1
DG38	What kind of distribution is this? <br><img src='Screenshot_2015-09-16-08-18-06.png'/>	uniform symmetric		c1
DG39	What kind of distribution is this? <br><img src='Screenshot_2015-09-16-08-17-58.png'/>	unimodal, skewed left		c1

DG40	What kind of distribution is this? <br><img src='Screenshot_2015-09-16-08-17-51.png'/>	bimodal asymmetric		c1
DG41	What kind of distribution is this? <br><img src='Screenshot_2015-09-16-08-17-43.png'/>	unimodal, skewed right		c1
DG42	"<br /><img src=""Screenshot_2015-09-16-08-17-26~2.png"" /><br />name of"	bimodal symmetric,<div>graph of</div>	Y	c1
DG43	What kind of distribution is this? <br><img src='Screenshot_2015-09-16-08-17-19.png'/>	unimodal symmetric		c1
DG44	given this data, where is it skewed? why? how does it look?<img src='Screenshot_2015-09-16-08-33-31~2.png'/>	to the left because the mean is less than the median.<img src='Screenshot_2015-09-16-08-36-22~2.png'/>		c1
DG45	range()	<div><div>difference between the lowest and highest values</div></div>	Y	c1
DG46	Frequency is....<br>density is....	Frequency is the number of times you observe a value in a particular bin<br>Density is the contnuous distribution that a discrete data set represents. i.e. histogram with a curve on top of it.<br>		c1
DG47	Why is it better to use density instead of frequency?	because density does not depend on sample size.		c1
DG48	create an image like <img src='Screenshot_2015-09-16-08-50-45~2.png'/>	<img src='Screenshot_2015-09-16-08-57-52~2.png'/>		c1
DG49	what do you say when you see this<br><img src='Screenshot_2015-09-19-10-15-46~2.png'/>	the Normal model with a mean of Mu and a standard deviation Sigma		c1

DG50	equation of the probability density function. PDF	<img src='Screenshot_2015-09-19-10-15-46~3.png'/>		c1
DG51	what is the standard normal model? <br>how you graph it?<br>	N(0,1)<br><img src='Screenshot_2015-09-19-10-37-52.png'/>		c1
DG52	z-score tells us..	how many standard deviations away of the mean a certain value is		c1
DG53	what is the formula for z-score?<br>what do s a positive z-score mean?<br>what does a negative z-score mean?	<img src='Screenshot_2015-09-19-10-45-56~2.png'/><br>positive is above the mean<br>negative is below the mean		c1
DG54	how do you create this graph? <img src='Screenshot_2015-09-19-10-50-43~2.png'/>	<img src='Screenshot_2015-09-19-10-50-43~3.png'/>		c1
DG55	68-95-99.7 rule means	68% of your values will be between -1 and +1 standard deviations of the mean.<br>95% of your values will be at 2 std deviations of the mean.<br>99.7% are between -3 and +3 std deviations of the mean.	y	c1
DG56	what does this code draw?<img src='Screenshot_2015-09-19-11-00-57~2.png'/><br>what does it represent?	<img src='Screenshot_2015-09-19-11-00-57~3.png'/><br>it represents the 68-95-99% rule		c1
DG57	how do you graphically represent the 68-95-99.7 rule?	<img src='Screenshot_2015-09-19-11-05-58.png'/>		c1
DG58	calculates the z-score of a vector	"scale()<br /><img src=""Screenshot_2015-09-19-11-18-00.png"" />"		c1
DG59	"creates this graphics<div><img src=""Screenshot_2015-09-19-11-23-51~2.png"" /></div>"	<img src='Screenshot_2015-09-19-11-23-51~3.png'/>	y	c1

DG60	what % of students get below a 50?<br>for the Normal model with mean of 47.3 and SD of 9.3?	<img src='Screenshot_2015-09-19-11-37-18~2.png'/><br><img src='Screenshot_2015-09-19-11-37-11~2.png'/>		c1
DG63	<img src='Screenshot_2015-09-19-11-41-42~2.png'/>	<img src='Screenshot_2015-09-19-11-42-07~3.png'/>		c1
DG64	What is the percentile, if we score 85 in an exam where the results where distributed with N(78, 5)?	<img src='Screenshot_2015-09-19-12-01-15~2.png'/>		c1

DG66	to create a bar chart you can use either ______ or a ________	vector or a <br>column from dataset		c2

DG68	how do they make this b graph and how do they n make the color s match the column?<br><img src='Screenshot_2015-09-19-12-31-07~2.png'/>	<img src='Screenshot_2015-09-19-12-31-28~2.png'/>		c2
DG69	creates a contingency table whose <b>rows</b> will be  the <b>color</b> data of the mm.df dataframe and <b>columns</b> will be the <b>defects</b>.	<img src='Screenshot_2015-09-19-13-11-55~2.png'/><br><img src='Screenshot_2015-09-19-13-12-20~2.png'/>		c2

DG70	<img src='Screenshot_2015-09-19-13-22-04~2.png'/>	<img src='Screenshot_2015-09-19-13-22-11~3.png'/>		c2

DG72	creates unimodal distribution skewed to the left	Beta function with alpha=5 and beta=2<br>x <- rbeta(5000,5,2)<br>hist(x)<br><img src='img_2015101506484928927469.png'/>		c2
DG73	"In this two lines what does freq=FALSE mean?<br><div>hist(x,col=""gray"",xlab=""Our Randomly Sampled Values"", ylab=""Frequency of Ocurrence"", main=""Frequency"")</div><div>hist(x,freq=FALSE, col=""gray"",xlab=""Our Randomly Sampled Values"", ylab=""Density"", main=""Density"")</div><div>lines(density(x),lty=""dotted"", lwd=3)</div><div><br></div>"	plot in terms of Density not frequency.<br>you can see this in the y axis of the graph that gets generated.		c2
DG74	Generates a Bimodal Symmetric	"x &lt;-c(rnorm(100000,0,1), rnorm(100000,4,1))<div>hist(x)</div><div><img src=""paste-9393093476353.jpg"" /></div>"		c2
DG75	Meaning of Skewed Right?	"If the distribution is skewed to the right, meaning that it has a tail stretching out along the right side of the x-axis... the median is pulled to the right.<div><img src=""paste-7907034791937.jpg"" /></div>"		c2
DG76	this data looks<br>x &lt;- c(rnorm(1000,0,1), rnorm(1000,4,1))	code to generate a Bimodal Symmetric		c2
DG77	par (usr = c (-1, 21, 0, 1))	vector&nbsp;&nbsp;with coordinates&nbsp;c (x1, x2, y1, y2)<div>of the user  plotting region.</div>	y	c2
DG78	par (usr = c (-1, 21, 0, 1))<br>could fail...	if you have not previously called plot.new()	y	c2
DG79	"""oficial"" name of the correlation  coefficient"	Person-Moment Correlation Coefficient,<br> also known as, PPMC, PCC or Person's r.	y	c2

DG80	"How can you create a data subset from the M&amp;M's dataframe to create these conditional distributions?<div><img src=""r.png"" /></div>"	"<div>&gt; defects.for.greens &lt;- table(mnms[mnms<font color=""#aa0000"">$color==""G"",</font>]$defect)</div><div>&gt; colors.for.chippedorcracked &lt;- table(mnms[mnms<font color=""#aa0000"">$defect==""C""</font>,]$color)</div><div>&gt; par(mfrow=c(1,2))</div><div>&gt; barplot(defects.for.greens,main=""Conditional Distribution of Defects</div><div>(Green M&amp;Ms)"")</div><div>&gt; barplot(colors.for.chippedorcracked,main=""Conditional Distribution of</div><div>Colors (Chipped/Cracked)"")&nbsp;</div>"		c2
DG81	conditional distribution	the distribution of how frequently one of the categorical variables is observed given that you specifically set the other categorical variable to assume one specific value.<div><br></div><div>e.g. The distribution of defects over all the Blue M&amp;M's</div>	y	c2
DG82	"Within each contingency table there are two <span class=cloze>[...]</span> distributions: one distribution for each of the total observed fequencies for each of the two <span class=cloze>[...]</span> variables.<div><br /></div><div>e.g. There are one for colors and other to defects here:</div><div>&gt; table[mm$colors, mm$defect]</div><div><img src=""r (1).png"" /></div>"	"Within each contingency table there are two <span class=cloze>marginal</span> distributions: one distribution for each of the total observed fequencies for each of the two <span class=cloze>categorical</span> variables.<div><br /></div><div>e.g. There are one for colors and other to defects here:</div><div>&gt; table[mm$colors, mm$defect]</div><div><img src=""r (1).png"" /></div><br><br>"		c2
DG83	Create a contingency table for the M&amp;M's dataset to show how many colors are per defect.	"&gt; table(mm$color, mm$defect)<div><img src=""r (2).png"" /></div>"	y	c2
DG84	"function to create ""more fancy"" contingency tables"	"CrossTable() in the ""gmodels"" package."	y	c2
DG85	<div><span class=cloze>[...]</span> provide a way to display the frequency and relative frequencies of&nbsp;observations, which are classified according to two categorical variables.</div>	<div><span class=cloze>Contingency tables</span> provide a way to display the frequency and relative frequencies of&nbsp;observations, which are classified according to two categorical variables.</div><br><br>		c2
DG86	<div>&gt; pairs(my.data[,1:10])</div>	"<div>pairs of scatterplots for all rows,</div><div> only for the first 10 columns in my.data.</div><div><img src=""r (3).png"" /></div>"	y	c2
DG87	tells R that a data is a character	as.character()	y	c2
DG88	<div>You can generate a <span class=cloze>[...]</span> to show the relationship between any two <span class=cloze>[...]</span>&nbsp;variables. The simplest scatterplot is generated by typing <span class=cloze>[...]</span>.</div>	<div>You can generate a <span class=cloze>scatterplot</span> to show the relationship between any two <span class=cloze>quantitative</span>&nbsp;variables. The simplest scatterplot is generated by typing <span class=cloze>plot(x,y)</span>.</div><br><br>		c2
DG89	formula for the correlation coeficient <i>r</i>	"<div>is the covariance of the data (<img class=latex src=""latex-33b057b42bca3a55faeed06769e438f5dbacfdd0.png"">) divided by the standard deviation of the x coordinates (<img class=latex src=""latex-266d29153952f3de73493f4096b7183180a562f0.png"">) multiplied by the standard deviation of the y coordinates (<img class=latex src=""latex-cb5a2f428f39704cf09d7434383fa5306094a7f6.png"">):</div><div><br></div><img class=latex src=""latex-feb196f0afcd57a4f2286c475b2b90b3c9168e5e.png""><div><br></div>"	y	c2

DG90	get a sense of the scattered-ness of a set of points	by finding the <i>covariance</i>&nbsp;and the <i>correlation</i>&nbsp;<div><br /><div><br /></div></div>	Y	c2
DG91	Talking about scattered-ness:&nbsp;Quantitative variables are positively related if, when one <span class=cloze>[...]</span>, the other one<span class=cloze>[...]</span>.&nbsp;<div><div>Quantitative variables are negatively related if, when one <span class=cloze>[...]</span>, the other one</div><div><span class=cloze>[...]</span>.</div></div>	Talking about scattered-ness:&nbsp;Quantitative variables are positively related if, when one <span class=cloze>increases</span>, the other one<span class=cloze>&nbsp;also increases</span>.&nbsp;<div><div>Quantitative variables are negatively related if, when one <span class=cloze>increases</span>, the other one</div><div><span class=cloze>decreases</span>.</div></div><br><br>		c2
DG93	calculate the <i>P-value</i>&nbsp;of a dataset	&gt; shapiro.test(my.data$V1)	Y	c2
DG92	<i>P-value</i>&nbsp;less than 0.5 means	we can reject the null hypotesis that the data are normal.	Y	c2

DG94	What is the formula of the <i>covariance</i>&nbsp;?	"Covariance is computed like this: for the first point in your data set, compute the difference between the x coordinate of that point and the mean of all the x coordinates, then do the same for y and multiply those two values together. Then, do that for all the rest of the points! Add up the n products that you computed. Then, when it's all added up, divide by one less than the total number of points (n - 1):&nbsp;<div><img class=latex src=""latex-2b49ccca782688cd493d06d94635797e5d85ae32.png""></div>"		c2
DG95	In a qqplot each point is a..	"representation of the difference between the actual test score and the test score we would have observed if that score came from a perfectly normal distribution.<div><img src=""r (4).png"" /></div>"		c2
DG96	How do you plot a <i>normal probability plot (Q-Q Plot) </i>of a variable from a dataframe?	"&gt; qqnorm(my.data$V1)<div>&gt; qqline(my.data$V1)</div><div><img src=""r (4).png"" /></div>"		c2
DG97	objective of the normal probability plot (Q-Q plot)	gives you a quick diagnostic that reveals whether the data are distributed normally or not.	y	c2
DG98	Q-Q plot means	Quantile-Quantile plot.	y	c2
DG99	"A <span class=cloze>[...]</span> is a sorted bar chart that displays the <span class=cloze>[...]</span> of occurrences that fall in different categories, from greatest frequency on the left to least frequency on the right, with an overlaid line chart that plots the cumulative percentage of occurrences.<div><img src=""r (6).png"" /></div>"	"A <span class=cloze>Pareto Chart</span> is a sorted bar chart that displays the <span class=cloze>frequency (or count)</span> of occurrences that fall in different categories, from greatest frequency on the left to least frequency on the right, with an overlaid line chart that plots the cumulative percentage of occurrences.<div><img src=""r (6).png"" /></div><br><br>"		c2
DG100	On a Pareto Chart the vertical axis <span class=cloze>[...]</span> of the chart shows frequency (or count), and the vertical axis <span class=cloze>[...]</span> of the chart shows the cumulative percentage. &nbsp;	On a Pareto Chart the vertical axis <span class=cloze>on the left</span> of the chart shows frequency (or count), and the vertical axis <span class=cloze>on the right</span> of the chart shows the cumulative percentage. &nbsp;<br><br>		c2
DG101	How can you import a package directly from GitHub?	"&gt; install.packages(""devtools"")<div>&gt; library(devtools)</div><div>&gt; devtools::install_github(""package.name"")</div>"		c2
DG102	What are the three principles that a pie chart must follow?	"1) The slices adhere to the <b>Principle Area</b>.<div>2) The slices add up to exactly <b>100%</b></div><div>3) There are <b>no slight variations</b> between slices that are not easily distinguished on the pie chart.</div><div><img src=""paste-9504762626049.jpg"" /></div>"		c2
DG103	In the M&amp;M dataframe,  obtain the count of colors and plot a pie chart	"&gt; my.pie &lt;- table(mm$color)<div>&gt; pie(my.pie)</div><div><img src=""paste-12442520256513.jpg"" /></div>"	y	c2
DG104	from the M&amp;M  CSV file,<br> obtain a frequency table showing the count of defects	"&gt; data &lt;- read.csv(""mnm.csv"")<div>&gt; table(data$defect)</div><div><img src=""paste-12979391168513.jpg"" /></div>"	y	c2
DG105	Good pie charts adhere to the <span class=cloze>[...]</span> Principle. This means that each slice of the pie has an area that corresponds to <span class=cloze>[...]</span> that make up that slice. If 25% of your observations fall into one category, that category should make up exactly a quarter of the pie. No more, no less.	Good pie charts adhere to the <span class=cloze>Area</span> Principle. This means that each slice of the pie has an area that corresponds to <span class=cloze>the percentage of the total number of observations</span> that make up that slice. If 25% of your observations fall into one category, that category should make up exactly a quarter of the pie. No more, no less.<br><br>		c2
DG106	In a pie chart, membership into a category should be <span class=cloze>[...]</span>. So there are no observations that appear in multiple slices.	In a pie chart, membership into a category should be <strong><span class=cloze>mutually exclusive DataGallos Rules!<span></strong>. So there are no observations that appear in multiple slices.<br><br>		c2
ag001	what is the difference between fivenum() and summary()	summary also displays the mean()		


jm114a	Code for creating a comparative box plot...	"&gt; boxplot(score~year, data = allscores)<div><img src=""paste-3607772528643.jpg"" /></div>"	Y	c2
jm113a	Boxplots provide an alternative to ______; they are useful to visualize the distribution of _______ variables.	(1) histograms &nbsp; &nbsp; (2) quantitative		c2
jm113b	elements are indicated in a boxplot	"1. first and third quartiles<div>2. The median</div><div>3. Any significant outliers within the distribution</div> <img src=""boxplot with outliers.png"" />"	Y	c2
jm113c	Comparative box plots are useful to...	...compare many distributions to one another.		c2
jm111a	Plot a simple box plot...	boxplot(my.variable)		c2


jm107a	In a box plot, what are outliers?	values bellow the lower limit as well as the values above the upper limit.<br><img src='Screenshot_2015-10-14-06-57-45~2.png'/>		c2
jm107b	How the lower an upper limits are calculated in a box plot?	<div>IQR = Q3 - Q1 #Interquartile Range</div>Lower Limit = Q1 - 1.5 x IQR<div>Upper Limit = Q3 + 1.5 x IQR</div><br><img src='images (2).png'/>		c2
jm107c	What is the rationale for the IQR (Interquartile range)?	Because we know that 25% of our observations are below Q1, and another 25% of observations are above Q3, this implies that exactly half of our observations - 50% of our observations - will fall between Q1 and Q3. And that's the purpose of the box in the box plot: to clearly show the space within which half of our values lie, while clearly showing us the median (indicated by a heavy line somewhere near the middle of our box). &nbsp;		c2
jm105a	What kind of information is given by the summary() function?	"This function gives descriptive statistics:<div>Minimum value (Min.)</div><div>1st. Quartile (1st. Qu.)</div><div>Median</div><div>Mean</div><div>3rd. Quartile (3rd. Qu.)</div><div>Maximum value (Max.)</div><div><img src=""paste-10423885627393.jpg"" /></div>"		c2
jm105b	five number summary	descriptive statistics that give a general idea of the sample.<br />min, q1, median, q3, max<br /><br />	y	c2
jm104a	purpose of segmented bar charts?	"visualize conditional distributions, which show the distribution of one variable for only the cases that match a second condition.<div><img src=""paste-11300058955779.jpg"" /></div>"		c2
jm101a	Plot a segmented bar chart...	"# Choosing columns for categories (x axis)<div>barplot(prop.table(my.data, 2))</div><div><img src=""paste-11682311045121.jpg"" /></div><div># Chossing rows for categories (x axis)</div><div>barplot(prop.table(my.data, 1))</div><div><img src=""paste-11712375816193.jpg"" /></div>"		c2
jm99a	How do you need to have your data to create a segmented bar chart?	organized in terms of two categorical variables.		c2
jm99c	The information contained in one of the bars in a segmented bar chart is called...	... a <b>conditional distribution</b> since it's the distribution of observed cases for which the condition of an additional categorical variable having a given value is met.		c2
jm95a	What is the rationale of the <i>kernel density estimatio</i>n in a histogram plotted in terms of density?	"It describes one process for trying to estimate the continuous distribution that a discrete data set represents.<div><img src=""paste-14096082665473.jpg"" /></div>"		c2
jm95b	Plot a histogram in terms of density with a curve showing the <i>kernel density estimation</i>...	"data &lt;- rbeta(5000, 5, 2)<div><div>hist(</div><div><span class=""Apple-tab-span"" style=""white-space:pre""> </span>data,</div><div><span class=""Apple-tab-span"" style=""white-space:pre""> </span>col = ""orange"",</div><div><span class=""Apple-tab-span"" style=""white-space:pre""> </span>xlab = ""Our Randomly Sampled Values"",</div><div><span class=""Apple-tab-span"" style=""white-space:pre""> </span>ylab = ""Density"",</div><div><span class=""Apple-tab-span"" style=""white-space:pre""> </span>main = ""Histogram in Terms of Density"",</div><div><span class=""Apple-tab-span"" style=""white-space:pre""> </span>breaks = 10,</div><div><span class=""Apple-tab-span"" style=""white-space:pre""> </span><b><font color=""#aa0000"">freq = FALSE</font></b></div><div>)</div><div>lines<b><font color=""#aa0000"">(density(</font>data<font color=""#aa0000"">)</font></b>, lty = ""dotted"", lwd = 3)</div></div><div><img src=""paste-16058882719745.jpg"" /></div>"		c2
jm93a	Plot a simple histogram of random generated data...	"&gt; data &lt;- rnorm(5000, 5, 2)<div>&gt; hist(data)</div><div><img src=""paste-16252156248065.jpg"" /></div>"		c2
jm84a	To create a _____ chart you need categorical data, which is a collection of numbers that represent _____ (or counts).	(1) bar &nbsp; &nbsp; (2) frequencies		c2
jm84b	Bar charts are an alternative to...	...pie charts.		c2
jm85a	Plot a simple bar chart with labels for each category and the axes...	"mm.counts &lt;- c(12, 6, 8, 10, 6, 7)<div>names(mm.counts) &lt;- c(""blue"", ""brown"", ""green"", ""orange"", ""red"", ""yellow"")</div><div>mm.colors &lt;-&nbsp;&nbsp;c(""blue"", ""brown"", ""green"", ""orange"", ""red"", ""yellow"")</div><div>barplot(mm.counts, main = ""My M&amp;M Color Distribution"", xlab = ""M&amp;M Colors"", ylab = ""Number of M&amp;M in Bag"", col = mm.colors)</div><div><img src=""paste-17145509445633.jpg"" /></div>"		c2
DG20	plots the CDF from a collection of values x	pnorm(x, mean, SD)		c1
DG65	Let's say we are responsible for buying manufactured parts from one of our suppliers, to use in assemblies that we sell to our customers. To work in our assembly, each part has to be within 0.01 inches of the target length of 3.0 inches. If our supplier tells us that the population of their parts has a mean length of 3.0 inches with a standard deviation of 0.005 inches, what proportion of the parts that we buy can we expect to not be able to use? (This has implications for how many parts we order, and what price we will negotiate with our supplier).	"We know that the length of the parts is distributed as N(3.0, 0.005). We can't use parts that are shorter than (3.0 - 0.01 = 2.99 inches), nor can we use parts that are longer than (3.0 + 0.01 = 3.01 inches). This picture is drawn with shadenorm(below=2.99,above=3.01,color=""black"",mu=3,sig=0.005):<div><img src=""paste-9015136354305.jpg"" /></div><div>Since pnorm always gives us the area to the left of a certain point, let's use it to find out the area in the left tail. First, let's calculate a z score for x=2.99:</div><div><br /></div><div>[$]z=\frac{x-\mu}{\sigma}=\frac{2.99-3.00}{0.005}=-2[/$]</div><div><br /></div><div>Using the 68-95-99.7 rule, we know the area we're looking for will be about 5% (since 95% of the area is contained inside z=-2 and z=+2). So let's look up to see what the area is exactly, multiplying by 2 since we need to include the area in both tails:</div><div><br /></div><div>&gt; pnorm(2.99,mean=3,sd=0.005) * 2</div><div>[1] 0.04550026&nbsp;</div><div><br /></div><div><div>Approximately 4.5% of the parts that we order won't be within our&nbsp;required specifications.</div></div>"		c1
DG67	how do you create bar charts using the vector approach?	"mm.counts &lt;- c(12,8,6,9)<br>mm.colors &lt;- c(""red"",""green""...)<br>barplot(mm.counts,  names.arg=mm.colors, col=mm.colors)"		c2
DG71	<img src='Screenshot_2015-09-19-13-22-11~2.png'/>	<img src='Screenshot_2015-09-19-13-23-00~2.png'/>		c2



jm162a	In selecting a sample, <b>randomness</b> means...	all the cases of the sample will be a <b>representative of the population.</b>	Y	c3
jm164a	People are just notoriously bad generating random numbers	To randomize something you will have to use a Table of Random Numbers or a computer program.	Y	c3
jm165a	The Law of Large Numbers (LLN)	When you look at all your outcomes from multiple trials, the results will converge on some expected value, even if the individual outcomes are random (like flipping a coin 10 or 10 million times).	y	c3
jm166a	benefit of performing statistical tests 	let us understand a population by observing smaller samples.		c3
jm166b	Sample Statistics (never sample parameters)	<div>Values that pertains to samples</div><div><br /></div>[$]<div>\overline{x} (Mean) \\<div>s (Standard \quad Deviation) \\</div><div>s^{2} (Variance) \\</div><div>\hat{p} (Proportion \quad of \quad&nbsp;something \quad&nbsp;occuring) \\</div><div>\hat{q} (Proportion \quad&nbsp;of \quad&nbsp;something \quad&nbsp;not \quad&nbsp;ocurring)[/$]</div></div>	y	c3
jm166c	Population Parameters (never population statistics)	Values that pertain to populations<div>[$]\mu (Mean) \\</div><div>\sigma (Standard \quad&nbsp;Deviation) \\</div><div>\sigma^{2} (Variance) \\</div><div>p (Proportion \quad&nbsp;of \quad&nbsp;something \quad&nbsp;occuring) \\</div><div>q (Proportion \quad&nbsp;of \quad&nbsp;something \quad&nbsp;not \quad&nbsp;ocurring)[/$]</div>	y	c3
jm167a	Representative sample	A sample that have characteristics that are similar to those of the entire population	y	c3

jm176k	Howthorne Effect	A type of reactivity in which individuals modify or improve an aspect of their behavior in response to their awareness of being observed.	y	c3
jm177a	An observational study can be appropriate.	If you are just trying to figure out the <b>relationship</b> between different variables or whether there is an <b>association</b>.	y	c3
jm177b	Is it possible to manipulate variables in an observational study?	NO, you do not manipulate variables in the environment at all.		c3
jm177c	A <b>prospective</b>&nbsp;observational study.	A study where participants are tracked or followed into the future.	y	c3
jm177d	A <b>retrospective</b>&nbsp;observational study.	A study where you are looking back on data that was collected in the past.	y	c3
jm177e	A <b>sample survey</b>&nbsp;(a type of observational study)	A study where you use a method for collecting information to find out whether there is a relationship or association between two or more parameters.	y	c3
jm177f	In prospective observational studies, what means that subjects are followed <i>longitudinally</i>?	That's a big word that's actually simple - it just means over a span of time. So for example, if you kept track of your grades over the course of your education and examined them for patterns, you could say that you conducted a longitudinal study of your academic performance.		c3
jm179a	Correlation does not imply _____, specially in _____.	(1) causation &nbsp; &nbsp; (2) observational studies		c3
jm179b	What are some of the differences between an observational study and an experiment?	<div>Just ask yourself these questions:</div><div>Is there a control group?</div><div>Is there (at least one) treatment group?</div><div>Did the researcher randomly assign subjects to one of these groups?</div><div>Did the researcher manipulate the environment in some way?</div><div><b>If you answer NO</b> to any of these questions, then you performing an <b>observational study</b>, never an experiment and you can't claim that one factor causes, effects, impacts, or influences changes in another factor.&nbsp;</div>		c3
jm180a	Even though a correlation or a relationship does not imply causation	It can suggest that there is a <b>causal relationship</b>. But you'll have to do an experiment to be able to say this for sure.	y	c3
jm101b	Given this data table(mm$color, mm$defect) plot a segmented bar chart by color...	"<div># Chossing rows for categories (x axis)</div><div>barplot(prop.table(my.data, <b><font color=""#aa0000"">1</font></b>))</div><div><img src=""paste-11712375816193.jpg"" /></div>"		c2
jm99b	Segmented bar charts are a _____ distribution; they are usually scalled so all the bars reach the _____ mark on the vertical axis.	(1)&nbsp;frequency &nbsp; &nbsp; (2) 100%		c2
jm173d	Both, experiments and observational studies, may undercover&nbsp;_____.	(1) a correlation		c3
jm179a	Correlation does not imply _____, specially in _____.	(1)&nbsp;causation &nbsp; &nbsp; (2)&nbsp;observational studies		c3
jm181a	In Dr. R's 12 Steps you only need to describe steprs 1 through 5 _____ (the will be the same for all _____).	(1)&nbsp;ONCE &nbsp; &nbsp; (2)&nbsp;research questions		c3
jm181b	In Dr. R's 12 Steps you will need to perform Steps 6 through 12 for _____.	(1) EACH research question individually		c3