invest_3ed.pdf

X optional you can right click on graph select panel

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x Optional : You can right click on Graph, select Panel.., then Arrangement, setting 2 rows and 1 column. x Choose Graph > Boxplot . x Specify One Y, With Groups , press OK . x Enter the quantitative variable in the Graph variables box and then click in the Categorical variables box and enter the categorical variable. x Press Scale and check the Transpose value and category scales box. (Makes horizontal) x Press OK twice. And we can easily calculate separate summary statistics for the two conferences: Technology Detour Numerical Summaries In R To get separate descriptive statistics for each group: > iscamsummary(salary, conference) Note: This adds the second (explanatory) variable. You still have the option of entering “digits = ” to specify the number of significant digits you want displayed. In Minitab x Choose Stat > Basic Statistics > Display Descriptive Statistics . x Specify the quantitative variable in the Variables box and then click in the By variables box and specify the categorical variable. x Optional : Under Statistics you can choose which numbers are calculated.

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Chance/Rossman, 2015 ISCAM III Investigation 4.2 260 (c) Create a comparative graph and calculate the difference in the population mean salaries between the Eastern Conference and the Western Conference. (Notice that because the dataset lists salaries for all players in the NBA, you are working with a population and not a sample.) What symbols should you use to reference to this difference? (d) Also determine the population sizes and the population standard deviations and record these below, using appropriate symbols. (e ) Suppose we weren’t able to perform a census and only had resources to find the salaries for 40 players. How would you select the 40 players? (f) Use technology to select a random sample of 20 players from each league: Technology Detour Selecting Independent Random Samples In R take a random sample from each division > westsample = sample(salary[which(conference == "western")], 20) > eastsample = sample(salary[which(conference == "eastern")], 20) > salary.sample = data.frame(westsample, eastsample) > names(salary.sample) = c("western", "eastern") In Minitab MTB> unstack c5 c6 c7; m split the two conferences into two columns SUBC> subs c4. MTB> sample 20 c6 c8 m take a random sample from each column MTB> sample 20 c7 c9 MTB> name c8 'east sample' c9 'west sample' (g) Create comparative graphs and descriptive statistics for these sample data. [Note: Your data is currently “unstacked” which requires a different approach than “stacked data” in most software.] Technology Detour Unstacked data In R have to first stack the data > StackedData = stack(salary.sample[, c("western","eastern")]) > names(StackedData) = c("salary", "division") > iscamdotplot(StackedData\$salary, StackedData\$division) > iscamsummary(StackedData\$salary, StackedData\$division)
Chance/Rossman, 2015 ISCAM III Investigation 4.2 261 In Minitab x Select Graph > Dotplot , and then Multiple Y’s, Simple option, press OK.

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