Consider the following data for two variables, x and y. x 4 y 12 7 8 10 5 12 12 22 14 16 15 18 20 24 19 Enter negative values as negative numbers. a. Compute the standardized residuals for these data (to 2 decimals). Observation 1 Observation 2 Observation 3 Observation 4 Observation 5 Observation 6 Observation 7 Observation 8 Do the data include any outliers? Yes, there appear to be 2 outliers b. Compute the leverage values for these data (to 2 decimals). Observation 1 Observation 2 Observation 3 Observation 4 Observation 5 Observation 6 Observation 7 Observation 8

A weight-loss program wants to test how well their program is working. The company selects a simple random sample of 51 individual that have been using their program for 15 months. For each individual person, the company records the individual's weight when they started the program 15 months ago as an x-value. The subject's current weight is recorded as a y-value. Therefore, a data point such as (205, 190) would be for a specific person and it would indicate that the individual started the program weighing 205 pounds and currently weighs 190 pounds. In other words, they lost 15 pounds. When the company performed a regression analysis, they found a correlation coefficient of r = 0.707. This clearly shows there is strong correlation, which got the company excited. However, when they showed their data to a statistics professor, the professor pointed out that correlation was not the right tool to show that their program was effective. Correlation will NOT show whether or not there is…

An office supply company services copiers and tracks how many machines are serviced and the length of time (in minutes) for a service call. The data below for 11 clients is below.   Sum X = 46   Sum Y= 234  Sum XY = 5797       Sum X2 =1180         Sum Y2 =146608     1) Calculate SSYY, SSXX, SSXY   2) Calculate b0, b1   3) Interpret the estimated slope coefficient.

x i: 4 5 7 8 10 12 12 22         y i:12 14 16 15 18 20 24 19         b. Compute the standardized residuals for these data (to 2 decimals, if necessary). Enter negative values as negative numbers. Observation 1   Observation 2   Observation 3   Observation 4   Observation 5   Observation 6   Observation 7   Observation 8   - Do the data include any outliers? c. Compute the leverage values for these data (to 2 decimals). Enter negative values as negative numbers. Observation 1   Observation 2   Observation 3   Observation 4   Observation 5   Observation 6   Observation 7   Observation 8   Does there appear to be any influential observations in these data?

The basic hypothesis is that people who play sports are more likely than others to watch sport on TV. Your first task is to crosstabulate the data involving the playing and watching of sports and to determine the direction and strength of that relationship. Write out your findings about this basic relationship. Your next task is to determine the direction and strength of the partial relationships when you control for the gender of individuals.

2. Find the least linear fit to the following data: * * 2 3 2 -3 -3 -1 55

5. Which of the following is the assumption that the best way to describe the pattern of data is using a straight line? homoscedasticity   normality   linearity   restriction of range

c. Which of the following is the correct graph of the given data? -ОА. 10 1012 12 10 O C. 12- 81012 1012 Using the graph, explain the dramatic difference between the answers to parts (a) and (b). Choose the correct answer below. OA. The point (10,10) is not an outier, but it does have a strong effect on the least squares line and the correlation coefficient. OB. The point (10,10) does not have any effect on the least squares line and the correlation coefficient. OC. The point (10,10) is an outler that has a weak effect on the least squares line and the correlation coefficient. OD. The point (10,10) is an outlier that has a strong effect on the least squares line and the correlation coefficient.

Consider a two-dimensional scatterplot representing the relationship between two continuous variables. If the correlation coefficient is -1, then: a. All points lie in a straight line with a slope of -1. b. All points lie in a straight line with an unknown negative slope. c.  All points do not lie in a straight line but the best fitting regression line has a slope of -1 d.  There is a strong positive relationship between the two variables.