The data set AIRQ contains observations for 30 standard metropolitan statistical areas (SMSAS) in California for 1972 on the following variables. indicator of air quality (The lower the better) value added of companies (in 1000 US$) amount of rain (in inches) dummy variable, 1 for (SMSAS) at the coast, 0 for others population density (per square mile) average income per head (in US$) airq vala rain соas dens тedi reg airq vala rain coas dens medi df Number of obs 30 2.98 0.0313 0. 3829 0.2544 24.203 Source MS F( 5, Prob > F 24) = Model Residual 8723. 84625 14058.4538 5 1744.76925 24 585.768906 %3D R-squared Adj R-squared = Root MSE %3D Total 22782.3 29 785. 596552 %3D airq Coef. std. Err. t P>|t| [95% Conf. Interval] vala rain .0008834 .2506988 -33.3983 -.0010734 .0005545 111.9347 .0022562 .3435183 10.45752 .0016233 .0008503 15.33179 0. 39 0.73 -3.19 -0. 66 0. 699 0.473 0.004 0. 515 0. 521 0.000 -.0037731 -.458288 -54.98156 -.0044237 -.0012003 80. 29141 0055399 .9596857 -11. 81504 .0022769 .0023093 143. 5779 coas dens medi 0.65 7.30 _cons

The data set AIRQ contains observations for 30 standard metropolitan statistical areas (SMSAS) in California for 1972 on the following variables. indicator of air quality (The lower the better) value added of companies (in 1000 US$) amount of rain (in inches) dummy variable, 1 for (SMSAS) at the coast, 0 for others population density (per square mile) average income per head (in US$) airq vala rain соas dens тedi reg airq vala rain coas dens medi df Number of obs 30 2.98 0.0313 0. 3829 0.2544 24.203 Source MS F( 5, Prob > F 24) = Model Residual 8723. 84625 14058.4538 5 1744.76925 24 585.768906 %3D R-squared Adj R-squared = Root MSE %3D Total 22782.3 29 785. 596552 %3D airq Coef. std. Err. t P>|t| [95% Conf. Interval] vala rain .0008834 .2506988 -33.3983 -.0010734 .0005545 111.9347 .0022562 .3435183 10.45752 .0016233 .0008503 15.33179 0. 39 0.73 -3.19 -0. 66 0. 699 0.473 0.004 0. 515 0. 521 0.000 -.0037731 -.458288 -54.98156 -.0044237 -.0012003 80. 29141 0055399 .9596857 -11. 81504 .0022769 .0023093 143. 5779 coas dens medi 0.65 7.30 _cons

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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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

Explain how you can test the hypothesis that B2 related to average income per head is
0?

The data set AIRQ contains observations for 30 standard metropolitan statistical areas
(SMSAS) in California for 1972 on the following variables.
indicator of air quality (The lower the better)
value added of companies (in 1000 US$)
amount of rain (in inches)
dummy variable, 1 for (SMSAS) at the coast, 0 for others
population density (per square mile)
average income per head (in US$)
airq
vala
rain
сoas
dens
тedi
reg airq vala rain coas dens medi
df
Number of obs
30
2.98
Source
MS
F( 5,
Prob > F
24) =
Model
Residual
8723. 84625
14058.4538
1744.76925
0.0313
R-squared
Adj R-squared =
0. 3829
0.2544
24.203
24
585.768906
Total
22782.3
29
785. 596552
Root MSE
airq
Coef.
std. Err.
P>|t|
[95% Conf. Interval]
vala
rain
0. 699
.0008834
.2506988
-33. 3983
-.0010734
.0005545
0. 39
0.73
-3.19
-0. 66
0.65
.0055399
.9596857
-11. 81504
.0022562
-.0037731
-.458288
-54.98156
-.0044237
-.0012003
80. 29141
3435183
0.473
0. 004
0. 515
0. 521
сoas
10.45752
dens
medi
.0016233
.0022769
.0008503
.0023093
_cons
111.9347
15. 33179
7.30
0.000
143. 5779

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