Sales of Body | Price of Lens | Gender | Sales of Lens |
155 | $700 | 1 | 122 |
101 | 650 | 1 | 120 |
157 | 725 | 0 | 135 |
180 | 575 | 1 | 95 |
150 | 600 | 0 | 100 |
201 | 750 | 0 | 174 |
99 | 560 | 1 | 118 |
137 | 500 | 0 | 130 |
155 | 675 | 1 | 128 |
165 | 550 | 1 | 166 |
152 | 725 | 0 | 131 |
127 | 750 | 1 | 102 |
217 | 565 | 0 | 165 |
186 | 670 | 0 | 154 |
176 | 600 | 1 | 97 |
123 | 585 | 0 | 129 |
109 | 645 | 0 | 98 |
90 | 575 | 1 | 105 |
176 | 660 | 0 | 120 |
129 | 650 | 1 | 105 |
IF POSSIBLE USE PH STAT
h) Perform a Durbin-Watson analysis to assess the model for the presence of autocorrelation in this model for this data. Address the issues of the possible presence of both positive and negative autocorrelation in this model. As a part of this analysis, you will need to find the Durbin-Watson statistic for the problem at the 1% level of significance.
Hints: dL= 0.86 and dU = 1.27
using excel>addin>phstat>regreesion
we have
Regression Analysis | ||||||
Regression Statistics | ||||||
Multiple R | 0.5501 | |||||
R Square | 0.3027 | |||||
Adjusted R Square | 0.2206 | |||||
Standard Error | 21.4172 | |||||
Observations | 20 | |||||
ANOVA | ||||||
df | SS | MS | F | Significance F | ||
Regression | 2 | 3384.3893 | 1692.1946 | 3.6892 | 0.0467 | |
Residual | 17 | 7797.8107 | 458.6947 | |||
Total | 19 | 11182.2000 | ||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | 66.3946 | 46.0261 | 1.4425 | 0.1673 | -30.7120 | 163.5012 |
Sales of Body | 0.3821 | 0.1424 | 2.6838 | 0.0157 | 0.0817 | 0.6825 |
Price of Lens | 0.0020 | 0.0688 | 0.0292 | 0.9770 | -0.1431 | 0.1471 |
Durbin-Watson Calculations | |
Sum of Squared Difference of Residuals | 16402.20086 |
Sum of Squared Residuals | 7797.810731 |
Durbin-Watson Statistic | 2.103436647 |
the Durbin-Watson statistic for the problem is 2.10
there is negative autocorrelation.
Sales of Body Price of Lens Gender Sales of Lens 155 $700 1 122 101 650...
Sales of Body Price of Lens Gender Sales of Lens 155 $700 1 122 101 650 1 120 157 725 0 135 180 575 1 95 150 600 0 100 201 750 0 174 99 560 1 118 137 500 0 130 155 675 1 128 165 550 1 166 152 725 0 131 127 750 1 102 217 565 0 165 186 670 0 154 176 600 1 97 123 585 0 129 109 645 0 98 90 575...
Sales of Body Price of Lens Gender Sales of Lens 155 $700 1 122 101 650 1 120 157 725 0 135 180 575 1 95 150 600 0 100 201 750 0 174 99 560 1 118 137 500 0 130 155 675 1 128 165 550 1 166 152 725 0 131 127 750 1 102 217 565 0 165 186 670 0 154 176 600 1 97 123 585 0 129 109 645 0 98 90 575...
sales of body price of lens gender sales of lens 155 700 1 122 101 650 1 120 157 725 0 135 180 575 1 95 150 600 0 100 201 750 0 174 99 560 1 118 137 500 0 130 155 675 1 128 165 550 1 166 152 725 0 131 127 750 1 102 217 565 0 165 186 670 0 154 176 600 1 97 123 585 0 129 109 645 0 98 90...
Sales of Body Price of Lens Gender Sales of Lens 155 $700 1 122 101 650 1 120 157 725 0 135 180 575 1 95 150 600 0 100 201 750 0 174 99 560 1 118 137 500 0 130 155 675 1 128 165 550 1 166 ...
Sales of Body Price of Lens Gender Sales of Lens 155 $700 1 122 101 650 1 120 157 725 0 135 180 575 1 95 150 600 0 100 201 750 0 174 99 560 1 118 137 500 0 130 155 675 1 128 165 550 1 166 ...
Sales of Body Price of Lens Gender Sales of Lens 155 $700 1 122 101 650 1 120 157 725 0 135 180 575 1 95 150 600 0 100 201 750 0 174 99 560 1 118 137 500 0 130 155 675 1 128 165 550 1 166 ...
PLEASE IF POSSIBLE USE PH STAT Sales of Body Price of Lens Gender Sales of Lens 155 $700 1 122 101 650 1 120 157 725 0 135 180 575 1 95 150 600 0 100 201 750 0 174 99 560 1 118 137 500 0 130 155 675 1 128 165 550 1 166 152 725 0 131 127 750 1 102 217 565 0 165 186 670 0 154 176 600 1 97 123 585 0 129...
For this problem, you will add another independent variable, the variable “gender” to the data. In this case the variable is set to 0 if the purchaser of the camera body is a male and set to 1 if the purchaser of the camera body is not a male (is a female). Rerun the regression model using three independent variables, sales of the camera body (x1), price of the lens (x2) and gender (x3). The dependent variable is still number...
You have been asked to engage in an additional project
concerning sales of the Nikon D5 camera etc. Specifically, this
time you wish to study the sales of a certain camera lens by first
using two independent variables, sales of camera bodies and the
price of the lens. These are the independent variables for problem
one. For problem two, you will add the gender of the purchaser of
the equipment to the model. In the third question, you will examine...
The researcher studying this situation believes that the variable “number of camera bodies” may exhibit a quadratic relationship rather than a linear relationship. She decides to construct a new model with that variable assuming both a linear value and a quadratic value in the model. Only sales of the camera body should be used as an independent variable in this model. Do not use price of lens or gender. Let x = sales of the camera body. Let y =...