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Regression is a concept in Statistics used to measure the relationship between two variables, a response variable, and a predictor variable. The predictor variable has an influence over the response variable. Usually, it is the study of the cause and effect of one variable over the other variable. A rise in price and its effect of it on demand is one example where the concept of regression can be applied. The quantitative effect that one variable exerts over the other is studied in regression.  

Interestingly, the first study on regression was about the stature of parents and their children, conducted by Sir Francis Galton during the late 19th century. The heights of parents were compared. The study showed that a tall father had sons who were shorter than the father himself and the short father tended to have sons who were taller than him. The heights of the sons regressed to the mean. This means that the variables are imperfectly correlated. 

Regression analysis includes the following steps:

  • Statement of  problem
  • Selection of potentially relevant variables
  • Data collection
  • Model specification
  • Choice of the fitting method
  • Mode fitting
  • Model validation and criticism
  • Using the chosen model(s) for the selection of the posed problem. 

   Linear Regression Graphs

Why choose

Regression Formula:
Regression Equation(y) = a + bx
Slope(b) = (NΣXY – (ΣX)(ΣY)) / (NΣX2 – (ΣX)2)
Intercept(a) = (ΣY – b(ΣX)) / N
x and y are the variables.
b = the slope of the regression line
a = the intercept point of the regression line and the y-axis.
N = Number of values or elements
X = First Score
Y = Second Score
ΣXY = Sum of the product of first and Second Scores
ΣX = Sum of First Scores
ΣY = Sum of Second Scores
ΣX2 = Sum of square First Scores 

 Regression Example: To find the Simple/Linear Regression of


X Values

Y Values











To find the regression equation, we will first find the slope, and intercept and use it to form a regression equation…

Step 1: The count of the value is 5
N = 5

Step 2: Find XY, X2
See the below table 

X Value

Y Value





60 * 3.1 = 186

60 * 60 = 3600



61 * 3.6 = 219.6

61 * 61 = 3721



62 * 3.8 = 235.6

62 * 62 = 3844



63 * 4 = 252

63 * 63 = 3969



65 * 4.1 = 266.5

65 * 65 = 4225

    Step 3: Find ΣX, ΣY, ΣXY, ΣX2.

            ΣX = 311
ΣY = 18.6
ΣXY = 1159.7
ΣX2 = 19359

  Step 4: Substitute in the above slope formula given.
Slope(b) = (NΣXY – (ΣX)(ΣY)) / (NΣX2 – (ΣX)2)
= ((5)*(1159.7)-(311)*(18.6))/((5)*(19359)-(311)2)
= (5798.5 – 5784.6)/(96795 – 96721)
= 13.9/74
= 0.19

   Step 5: Now, again substitute in the above intercept formula given.
Intercept(a) = (ΣY – b(ΣX)) / N
= (18.6 – 0.19(311))/5
= (18.6 – 59.09)/5
= -40.49/5
= -8.098

  Step 6: Then substitute these values in the regression equation formula
Regression Equation(y) = a + bx
= -8.098 + 0.19x.

Suppose we want to know the approximate y value for the variable x = 64. Then we can substitute the value in the above equation.

            Regression Equation(y) = a + bx
= -8.098 + 0.19(64).
= -8.098 + 12.16
= 4.06 

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