Consider the following model you wish to estimate by using the OLS, Price = α + β sqrft + ε, where price is the house price in thousands of dollars and sqrft is size of house in square feet. The OLS result from Excel is as follows (see below). Suppose the house in the sample has sqrft=3,500. Find the predicted selling price for this house from the OLS regression estimation.
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.
Consider the following model you wish to estimate by using
the OLS,
Price = α + β sqrft + ε,
where price is the house price in thousands of dollars and sqrft is size of house in square feet. The OLS result from Excel is as follows (see below).
Suppose the house in the sample has sqrft=3,500. Find the predicted selling price for this house from the OLS regression estimation.
![SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
0.787906548
0.616387387
Standard Error
63.61708058
Observations
88
ANOVA
df
S
MS
Significance F
Regression
1
569801.0753
569801.0753
140.7912919
8.42341E-20
Residual
86
348053.433
4047.132941
Total
87
917854.5083
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
X Variable 1 (sqrft)
11.20414482
24.74260761
0.452827972
0.651812965
-37.98253022
60.39081986
0.140210977
0.011816643
11.86555064
8.42341E-20
0.116720269
0.163701686](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fee1a66ba-6084-4266-a778-1a4b071b2c0c%2F831467ea-969c-422b-ac56-37a79ac15a09%2Fg466odq_processed.jpeg&w=3840&q=75)
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