The following gives the number of accidents that occured on Floridaq State highway 101 during the last 4 months" month jan feb mar apr # of accidents 30 45 60 95 Using the least-squares regression method the trend equation for forecasting is (round your responses to two decimal places): ^Y=[__]+[___]X
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.
The following gives the number of accidents that occured on Floridaq State highway 101 during the last 4 months"
month jan feb mar apr
# of accidents 30 45 60 95
Using the least-squares regression method the trend equation for forecasting is (round your responses to two decimal places):
^Y=[__]+[___]X
Let the linear trend equation using the least-squares method be
Y = A + BX
where Y => No. of accidents
X => time in months
A => y-intercept
& B => Slope of linear trend line
Then we have the least-square regression estimates of A & B calculated from the given data as
Month | X | Y | (x - )2 | (x - )(y - ) |
January | 1 | 30 | 2.25 | 41.25 |
February | 2 | 45 | 0.25 | 6.25 |
March | 3 | 60 | 0.25 | 1.25 |
April | 4 | 95 | 2.25 | 56.25 |
Total | 10 | 230 | 5 | 105 |
Then
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