Sales Forecast Regression Exercise

INSEAD MBA Core Course UDJ
INSEAD

The Problem

A marketing manager wishes to forecast corporate sales for the coming year and to understand better the factors that influence them.

Sales Drivers

The manager believes that sales depend upon

personal disposable income, dealers' allowances prices, product development expenditures, capital investments, advertising, sales expenses, total industry advertising, and some random effects.

The Data

  1. Personal Disposable Income in Millions of Dollars. (PDI)

  2. Dealers' Allowances in Thousands of Dollars. (DEALS)

  3. Price in Dollars. (PRICE)

  4. Product Development in Thousands of Dollars. (R&D)

  5. Capital Investments in Thousands of Dollars. (INVEST)

  6. Advertising in Thousands of Dollars. (ADVERTIS)

  7. Sales Expenses in Thousands of Dollars. (EXPENSE)

  8. Total Industry Advertising in Thousands of Dollars. (TOTINDAD)

  9. Company Sales in Thousands of Dollars. (SALES)

Summary Statistics

Summary Statistics
PDI DEALS PRICE R.D INVEST ADVERTIS EXPENSE TOTINDAD SALES
min 268.000 104.000 56.200 7.740 -49.570 12.460 154.050 59.610 4033.130
25% 358.000 120.000 63.133 12.995 9.473 40.025 205.725 136.660 4767.525
median 415.000 130.000 69.075 19.655 60.310 65.790 224.250 242.995 5103.850
mean 406.100 129.125 69.372 19.587 57.508 60.334 232.727 238.441 5078.957
75% 455.250 138.000 75.455 24.907 113.860 83.208 266.335 315.253 5376.128
max 554.000 152.000 82.330 35.740 148.450 96.070 328.250 428.980 6180.060
std 70.765 12.845 7.821 7.880 61.538 26.087 43.924 110.407 513.181

Histogram of the Dependent Variable SALES

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Time Series of Sales

An interactive chart of the SALES time series (Place the mouse on the plot to see daily values, and zoom using click-and-drag with the mouse in the smaller graph below)

Correlation Matrix

PDI DEALS PRICE R.D INVEST ADVERTIS EXPENSE TOTINDAD SALES
PDI 1.00 -0.05198 0.5818 0.1628 0.1066 0.2057 0.9017 -0.05611 0.7301
DEALS -0.05198 1.00 0.0442 0.008086 -0.09995 -0.1096 -0.0409 -0.1528 0.01248
PRICE 0.5818 0.0442 1.00 0.4371 -0.08938 0.2764 0.6387 -0.2135 0.2949
R.D 0.1628 0.008086 0.4371 1.00 0.1673 0.1269 0.3628 -0.1295 0.04157
INVEST 0.1066 -0.09995 -0.08938 0.1673 1.00 0.238 0.1956 -0.1008 0.4239
ADVERTIS 0.2057 -0.1096 0.2764 0.1269 0.238 1.00 0.144 -0.1986 0.5306
EXPENSE 0.9017 -0.0409 0.6387 0.3628 0.1956 0.144 1.00 -0.04053 0.6675
TOTINDAD -0.05611 -0.1528 -0.2135 -0.1295 -0.1008 -0.1986 -0.04053 1.00 -0.1802
SALES 0.7301 0.01248 0.2949 0.04157 0.4239 0.5306 0.6675 -0.1802 1.00

Just one of the many possible plots...

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Our Regression Model

SALES = b1*PDI + b2*DEALS + b3*PRICE + b4*R.D + b5*INVEST + b6*ADVERTIS + b7*EXPENSE + b8*TOTINDAD

Regression Output

Regression Analysis - Dependent variableSALES
Estimate Std. Error t value Pr(> |t|)
(Intercept) 3027.6336 572.3599 5.29 0.0000
PDI 3.3723 1.4137 2.39 0.0234
DEALS 4.6953 3.0103 1.56 0.1290
PRICE -18.1112 7.4927 -2.42 0.0217
R.D -9.9033 6.0021 -1.65 0.1090
INVEST 1.6895 0.7157 2.36 0.0247
ADVERTIS 8.2907 1.6445 5.04 0.0000
EXPENSE 4.4434 2.5076 1.77 0.0862
TOTINDAD -0.4427 0.3646 -1.21 0.2339

R-squared: 0.8338 , Adjusted R-squared: 0.791

F-statistic: 19.4449 with 8 and 31 degrees of freedom

Residual standard error: 234.6363 with 31 degrees of freedom

The Residuals Plot (Time Series)

One of the many Residual plots...

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Histogram of the Residuals

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