Step 1 of 3: Write the estimated regression equation using the least squares estimates for b0�0 and b1�1. Round to four decimal places, if necessary. Step 2 of 3: What is the mean square error for the model shown in the output? Round to four decimal places, if necessary. Step 3 of 3: What is the standard error for the model shown in the output? Round to four decimal places, if necessary. <PrevConsider the following monthly revenue data for an up-and-coming service company.Month123456789Sales DataRevenue (Thousands of Dollars)315535533574628659697709789Month Revenue (Thousands of Dollars)1081911827128431385514849158581687017889>Next Regression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservations0.9309852070.8667334560.85784901960.6094321717ANOVAdfSSMS1 358,373.686275 358,373.6862753673.503268RegressionResidual 15 55,102.549020Total16 413,476.235294F97.55638151Coefficients Standard ErrorIntercept 453.79411765 30.7471441Month29.6372549t StatP-value14.758903012.43971E-103.000614017 9.877063405 5.87860E-08

We have given summary statistics of regression model as follows:

Answered: Step 1 of 3: Write the estimated…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

Step 1 of 3:

Write the estimated regression equation using the least squares estimates for b0�0 and b1�1. Round to four decimal places, if necessary.

Step 2 of 3:

What is the mean square error for the model shown in the output? Round to four decimal places, if necessary.

Step 3 of 3:

What is the standard error for the model shown in the output? Round to four decimal places, if necessary.

<
Prev
Consider the following monthly revenue data for an up-and-coming service company.
Month
1
2
3
4
5
6
7
8
9
Sales Data
Revenue (Thousands of Dollars)
315
535
533
574
628
659
697
709
789
Month Revenue (Thousands of Dollars)
10
819
11
827
12
843
13
855
14
849
15
858
16
870
17
889
>
Next

Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.930985207
0.866733456
0.857849019
60.60943217
17
ANOVA
df
SS
MS
1 358,373.686275 358,373.686275
3673.503268
Regression
Residual 15 55,102.549020
Total
16 413,476.235294
F
97.55638151
Coefficients Standard Error
Intercept 453.79411765 30.7471441
Month
29.6372549
t Stat
P-value
14.75890301
2.43971E-10
3.000614017 9.877063405 5.87860E-08

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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