# Quadratic Polynomial Model

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Question 4

Once again I have been asked to use SPSS but if you cannot help then we if you can provide the solutions and I will compare to the SPSS output that I obtain

A newly born baby was weighed weekly, the figure adopted in each case being the average of the weights on three successive days. Twenty such weights are shown below, recorded in ounces. Fit to the data a quadratic polynomial model. Test your model for adequacy of fit and for appropriateness. (Note: Want to estimate weight given the age of the baby.)

No. of week 1 2 3 4 5 6 7 8 9 10

Weight 141 144 148 150 158 161 166 170 175 181

No. of week 11 12 13 14 15 16 17 18 19 20

Weight 189 194 196 206 218 229 234 242 247 257

Question 5

A multiple regression model

E(Yt) = B0 + B1X1 + B2X2 + B3X3 + B4X4

A multiple regression model was used to study the number of requests for information received at a tourist information centre during the last 40 quarters. The data are shown in the table below, with most of the quarters omitted for brevity. Here Yt denotes thousands of requests received in quarter t, X1 is a variable representing linear trend, and X2 , X3 and X4 are indicator (dummy) variables representing the quarterly seasonal effects.

t: 1 2 3 4 ... 37 38 39 40

1 2 3 4 ... 37 38 39 40

0 1 0 0 ... 0 1 0 0

0 0 1 0 ... 0 0 1 0

0 0 0 1 ... 0 0 0 1

6.86 11.68 4.32 6.20 ... 14.34 19.99 11.22 13.28

The estimated regression equation is:

a. In which of the four quarters does the seasonal component reach its peak? In which does it reach its trough? Explain.

b. Predict the number of requests to be received in quarters 41 to 44 (inclusive). [For quarter 41, explain?]

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##### Solution Summary

The solution addresses the regression of fitting to the data a quadratic polynomial model. Test your model for adequacy of fit and for appropriateness. In which of the four quarters does the seasonal component reach its peak? In which does it reach its trough? Explain.

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Solution is attached.

Regression

Above tables are the regression outputs for your model. Your adj. R square is good, the sig. of the independent ...

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