Consider a linear demand model to explain the quantity demanded for a product: Q = a+ B, Price+ B, Income+ B,Advert+ s where Q = quantity sold, Price = price of the product, Income = purchaser's income, Advert = advertising. The following data was collected in year 2018. The company spends millions of money in advertisements. The company wants to know how advertisement as well how other factors affect the quantity of units sold. The results are as follows: Model Summary R 0.986 R? 0.973 Standard error of estimate 6.9872 Variables Coefficient Std error Sig Constant 205.862 19.354 0.000 Price -12.242 1.407 0.000 Income 1.414 0.422 0.015 Advert -3.344 1.798 0.112 Interpret and write a report based on the results obtained above.
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.
Can you interpret the Model summary table in detail.
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