EBK BUSINESS STATISTICS
7th Edition
ISBN: 9780134462783
Author: STEPHAN
Publisher: PEARSON CUSTOM PUB.(CONSIGNMENT)
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Suppose the athletic director at a university would like to develop a regression model to predict the point differential for games played by the men's basketball team. A point differential is the difference between the final points scored by two competing teams. A positive differential is a win, and a negative differential is a loss. For a random sample of games, the point differential was calculated, along with the number of assists, rebounds, turnovers, and personal fouls. Use the data in the accompanying table attached below to complete parts a through e below. Assume a = 0.05.
a) Using technology, construct a regression model using all three independent variables.
y = __ + (_)x1 + (_)x2 + (_)x3 + (_)x4
b) Test the significance of each independent variable using a= 0.10.
c) interpret the p-value for each independent variable.
d) Construxt a 90% confidence interval for the regression coefficients for each independent variable and interpret the meaning.
e) Using the results from…
A major brokerage company has an office in Miami, Florida. The manager of the office is evaluated based on the number of new clients generated each quarter. Data were collected that show the number of new customers added during each quarter between 2015 and 2018.
A multiple regression model was developed with the number of new customers as the dependent and the following four independent variables:
Period (1, …, 16): A variable that measures the trend;
Q1 = 1 for first quarter, Q1 = 0 otherwise;
Q2 = 1 for second quarter, Q2 = 0 otherwise;
Q3 = 1 for third quarter, Q3 = 0 otherwise.
Questions:
1. Explain each of the four slopes (Period, Q1, Q2, Q3).
2. How many new customers would you expect in the second quarter of the following year (2019)?
Retail price data for n = 60 hard disk drives were recently reported in a computer magazine. Three variables were recorded for each hard disk drive:
y = Retail PRICE (measured in dollars)
X1 = Microprocessor SPEED (measured in megahertz)
(Values in sample range from 10 to 40)
x 2 = CHIP size (measured in computer processing units)
(Values in sample range from 286 to 486)
A first-order regression model. was fit to the data. Part of the printout follows:
Parameter Estimates
T FOR 0
ERROR PARAMETER = 0 PROB>ITI
PARAMETER STANDARD
VARIABLE DF
ESTIMATE
INTERCEPT 1
-373.526392
1258.1243396 -0.297
0.7676
SPEED
1
104.838940
22.36298195 4 688
0.0001
сHP
1
3.571850
3.89422935
0.917
0.3629
Identify and interpret the estimate of B2-
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