c legression line on a quantity-price graph. Does the scatter of points look lin
ID: 3352989 • Letter: C
Question
c legression line on a quantity-price graph. Does the scatter of points look linear? c. Use a log-linear regression to estimate a demand curve of the form: kp What is the price e demand? Does this equation fit the data better than the linear equation in part (b)? Explain. 3. Your company's sales have been growing steadily over the last 17 quarters, as shown in the following table. Quarter Quantity Sales 137.1 140,9 42.7 149.3 154.4 158.1 164.8 172.0 181.3 Quantity Sales 103.2 105.7 Quarter 10 113.8 116.9 121.8 125.0 132.4 13 You wish to predict the next four quarters' sales.(You are aware that your product's sales have no seasonal component) a. Using regression techniques, find the linear time trend that best fits the sales data. How well does this equation fit the past data?Explanation / Answer
a)The fitted linear regression model is
Quarter= -19.971+.211Sales
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
-19.971
.832
-24.000
.000
Sales
.211
.006
.994
35.304
.000
a. Dependent Variable: Quarter
From this table, we can identify that R square is .988
which implies in full model sales can explain 98% of the variation in Quarter.
Model Summary
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
dimension0
1
.994a
.988
.987
.56874
a. Predictors: (Constant), Sales
b) Q = 99.34r0.034Sales
R² = 0.996
Here R square is .966,which implies in the full model, sales can explians 96% variation in Quarter.
So we can conclude that first model(linear equation model ) is better than this.Becuase R square of linear equations model is .988
c) Predicted values for 2 nd model is
475.4207
442.4157
518.6521
608.0254
712.7994
Predicted values for linear model is
18.2833
17.83679
18.82342
19.81005
20.79668
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
-19.971
.832
-24.000
.000
Sales
.211
.006
.994
35.304
.000
a. Dependent Variable: Quarter
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