Following regression output is for the consumption of water( millions of litres) in a city predicted by temperature (degree Celsius). Summary Output SSxx= 1003.875 SSxy= 16600.75 SSyy= 307135.5 average X= 24.375 average Y= 484.75 Adjusted R squared 0.8761 observations 8 please find: regressions statistics ANOVA intercept/ temperature ( coefficients, standard error, t stat, p value) regression line equation coefficient of correlation point estimate forecast 95% confidence interval estimate (UCL and LCL)
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.
Following regression output is for the consumption of water( millions of litres) in a city predicted by temperature (degree Celsius).
Summary Output
SSxx= 1003.875
SSxy= 16600.75
SSyy= 307135.5
average X= 24.375
average Y= 484.75
Adjusted R squared 0.8761
observations 8
please find:
regressions statistics
ANOVA
intercept/ temperature ( coefficients, standard error, t stat, p value)
regression line equation
coefficient of
point estimate forecast
95% confidence
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