Question:

Consider the linear regression model yi = β0 + β1xi + ∈i , i = 1, 2, … , 6, where β0 and β1 are unknown parameters and ∈i ’s are independent and identically distributed random variables having N(0, 1) distribution. The data on (xi, yi) are given in the following table.
xi1.02.02.53.03.54.5
yi2.03.03.54.25.05.4
If \(\hat{\beta}_0\) and \(\hat{\beta}_1\) are the least squares estimates of β0 and β1, respectively, based on the above data, then \(\hat{β}0 + \hat{β}1\) equals __________ (round off to 2 decimal places)

Updated On: Nov 24, 2025
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Correct Answer: 2

Solution and Explanation

1. Organize the Data and Calculate Summations

 

First, we create a table to calculate the necessary sums: ∑xi​, ∑yi​, ∑xi2​, and ∑xi​yi​.

 

ixi​yi​xi2​xi​yi​
11.02.01.002.00
22.03.04.006.00
32.53.56.258.75
43.04.29.0012.60
53.55.012.2517.50
64.55.420.2524.30
Sum16.523.152.7571.15

Number of observations, n=6.

 

2. Calculate the Means

 

xˉ=n∑xi​​=616.5​=2.75

yˉ​=n∑yi​​=623.1​=3.85

 

3. Calculate the Slope Estimate (β^​1​)

 

The formula for the slope is:

β^​1​=∑xi2​−nxˉ2∑xi​yi​−nxˉyˉ​​

Numerator (Sxy​):

71.15−6(2.75)(3.85)=71.15−63.525=7.625

Denominator (Sxx​):

52.75−6(2.75)2=52.75−6(7.5625)=52.75−45.375=7.375

Slope:

β^​1​=7.3757.625​≈1.0339

 

4. Calculate the Intercept Estimate (β^​0​)

 

The formula for the intercept is:

β^​0​=yˉ​−β^​1​xˉ

Substitute the values:

β^​0​=3.85−(1.0339)(2.75)

β^​0​=3.85−2.8432

β^​0​≈1.0068

 

5. Calculate the Final Sum (β^​0​+β^​1​)

 

β^​0​+β^​1​=1.0068+1.0339

β^​0​+β^​1​=2.0407

Rounding to two decimal places:

Answer: 2.04

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