Q.No.3 (a)Detect Heteroscedasticity from the given example output, also suggest some remedies. This example is from the 2020 Pakistan Health and Nutrition Survey using two variables: A person's...


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Q.No.3<br>(a)Detect Heteroscedasticity from the given example output, also suggest some remedies.<br>This example is from the 2020 Pakistan Health and Nutrition Survey using two variables: A<br>person's systolic blood pressure (systolic),A person's age, measured in years (age).<br>There are 9178 respondents in this survey. Systolic blood pressure measures the pressure in a<br>person's arteries when their heart beats (contracts and pumps blood). In this dataset, this<br>variable ranges from 70 to 240 with a mean of about 122 and a standard deviation of 18.14.<br>Age is measured in years, and in this dataset it ranges from 17 to 95 with a mean of about 49<br>and a standard deviation of 15.19.<br>Dependent Variable: SYSTOLIC BLOOD PRESSURE 1<br>2.5<br>Wion o<br>0.0<br>2.5<br>Regression Standardized Predicted Value<br>Regression Standardized Residual<br>

Extracted text: Q.No.3 (a)Detect Heteroscedasticity from the given example output, also suggest some remedies. This example is from the 2020 Pakistan Health and Nutrition Survey using two variables: A person's systolic blood pressure (systolic),A person's age, measured in years (age). There are 9178 respondents in this survey. Systolic blood pressure measures the pressure in a person's arteries when their heart beats (contracts and pumps blood). In this dataset, this variable ranges from 70 to 240 with a mean of about 122 and a standard deviation of 18.14. Age is measured in years, and in this dataset it ranges from 17 to 95 with a mean of about 49 and a standard deviation of 15.19. Dependent Variable: SYSTOLIC BLOOD PRESSURE 1 2.5 Wion o 0.0 2.5 Regression Standardized Predicted Value Regression Standardized Residual

Jun 10, 2022
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