Heights (cm) and weights (kg) are measured for 100 randomly selected adult males, and range from heights of 132 to 190 cm and weights of 38 to 150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield x = 167.60 cm, y = 81.56 kg, r = 0.369, P-value = 0.000, and y = −102 + 1.02x. Find the best predicted value of y (weight) given an adult male who is 187 cm tall. Use a 0.10 significance level.
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.
Heights (cm) and weights (kg) are measured for 100 randomly selected adult males, and
132 to
190 cm and weights of
38 to
150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield
x
=
167.60 cm,
y
=
81.56 kg, r
=
0.369, P-value
=
0.000, and
y
=
−102
+
1.02x. Find the best predicted value of
y (weight) given an adult male who is
187 cm tall. Use a
0.10 significance level.
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