BA 253: Linear Regression
BA 253: Linear Regression
A) Are advertising and sales related? Assume that the manager of a store has had five different
advertising campaigns and has kept track of the sales volume associated with each campaign.
|Ad |Sales |
|$3,000 |$14,000 |
|$5,000 |$26,000 |
|$8,000 |$29,000 |
|$10,000 |$46,000 |
|$13,000 |$48,000 |
a) Graph the data. Do they appear linear?
b) Calculate the linear regression equation.
c) Interpret the slope of the linear regression line.
d) Predict sales if the manager spends $9000 on ads.
e) Determine the coefficients of determination and
correlation, and interpret them.
f) At ( = 5%, test for the existence of correlation.
Do a) through f) on calculator, then on MS Excel
B) The data below represent the demand for a product over the past 5 months.
|Month |Demand |
|1 |187 |
|2 |167 |
|3 |171 |
|4 |155 |
|5 |144 |
a) Graph the data and the linear regression line.
b) Determine the linear regression equation.
c) Interpret the slope.
d) Predict demand in weeks 6, 7, and 8.
e) Determine r and r2, and interpret them.
f) At α = 5%, test for the existence of correlation.
BA 253: Linear Regression
A) Are advertising and sales related? Assume that the manager of a store has had five different
advertising campaigns and has kept track of the sales volume associated with each campaign.
|Ad |Sales |
|$3,000 |$14,000 |
|$5,000 |$26,000 |
|$8,000 |$29,000 |
|$10,000 |$46,000 |
|$13,000 |$48,000 |
a) Graph the data. Do they appear linear?
b) Calculate the linear regression equation.
c) Interpret the slope of the linear regression line.
d) Predict sales if the manager spends $9000 on ads.
e) Determine the coefficients of determination and
correlation, and interpret them.
f) At ( = 5%, test for the existence of correlation.
Do a) through f) on calculator, then on MS Excel
B) The data below represent the demand for a product over the past 5 months.
|Month |Demand |
|1 |187 |
|2 |167 |
|3 |171 |
|4 |155 |
|5 |144 |
a) Graph the data and the linear regression line.
b) Determine the linear regression equation.
c) Interpret the slope.
d) Predict demand in weeks 6, 7, and 8.
e) Determine r and r2, and interpret them.
f) At α = 5%, test for the existence of correlation.
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