Below is a scatterplot and some partial output for a simple linear regression mo

Question

Below is a scatterplot and some partial output for a simple linear regression model that relates sepal width (x) to sepal length () for the iris setosa flower, based on data collected by the famous statistician Sir Ronald Fisher. Measurements are in centimeters Scatterplot of Y vs X 6.0 5.5- 5.0 2.0 2.5 3.0 3.5 4.0 4.5 Pearson correlation of X and Y = 0.701 P-Value -0.000 Predictor Coef SE Coef Constant 2.7330 0.3429 7.97 0.000 0.66111 0.09903 6.68 0.000 S=0.258174 R-Sq=0.492 . Sepal width and sepal length are: a. Negatively correlated b. Positively Correlated c. Uncorrelated

Explanation / Answer

Solution11:

pearson corr coeff=0.70

strong positive relationship exists.

POSITIVELY CORRELATED

ANSWER B:

Solution12:

Regression Eq i s

y=2.7330+0.66111x

sepallength=2.77220+0.6611(sepal width)

slope=0.66111

Slope Intrepretation:

as sepal width ncreases by 1 unit sepal length increases by 0.6611 .

ANSWER :B

Solution13:

Substitute the petal width=3.2 cms in regression eq

sepallength=2.77220+0.6611(sepal width)

sepallength=2.77220+0.66(3.2)

=4.88772

ANSWER C

Solution14:

R sq=0.492

0.492*100=49.2% vraiation insepal length is explained by sepl width.

ANSWER (A)

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