WGU C955 Applied Probability and statistics Exam Study Guide
Pie chart numerical measure: percentages best used When displaying categories as parts of the whole Bar chart (bar graph) Category counts or frequencies best used when displaying counts or frequencies for each category individually Dot Plot 5-number summary (max, Q1, median, Q3, max) Smaller data set; useful to display the distribution and show gaps and outliers (quantitative) Stem Plot (stem-and-leaf plot) 5-number summary (max, Q1, median, Q3, max) Smaller data sets; useful to display distributions and potential outliers; has the advantage that it keeps data values displayed. (quantitative) Box Plot 5-number summary (max, Q1, median, Q3, max) Larger data sets; shows the 5-number summary, shows spread; ignores individual data values and the number of values (allowing to compare sets of different sizes) Histogram Based on the shape of the distribution, uses mean + standard deviation OR median + IQR to describe center and spread Larger data sets; shows shapes of distribution and potential outliers; ignores the individual data values slope-intercept form y = mx + b, where m is slope of the line and b is the y-intercept. Standard Deviation Rule: (68-95-99.7 Rule) 68% of the data is within 1 standard deviation of the mean. 95% of the data is within 2 standard deviations of the mean. 99.7% of the data is within 3 standard deviations of the mean positive correlation scatterplot reveals an "uphill trend." As the explanatory variable increases, the response variable increases. Negative Correlation scatterplot reveals a "downhill trend." As the explanatory variable increases, the response variable decreases. No Correlation scatterplot reveals no trend between the variables Correlation Coefficient (r) measures the direction and strength of the linear relationship between the variables. The closer r is to +1, the stronger the positive correlation. The closer r is to -1, the stronger the negative correlation. The closer r is to 0, the weaker the correlation. y=-0.082x+11 Using the linear regression we can predict that the monthly rent, y, for a home 50 miles from the center will be about:
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