Predict the age of abalones (in years) based on various physical features such as length and...

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Predict the age of abalones (in years) based on various physical features such as length and height. The target variable is the number of rings, where +1.5 gives the age in years. The aim is to build a regression model that can accurately estimate the age of abalones using these features. Sex (Categorical): Gender of the abalone, with M (Male), F (Female), and I (Infant). Length (Continuous): Denotes the longest shell measurement of the abalone in millimeters. Height (Continuous): Indicates the height of the abalone with meat in the shell, measured in millimeters. Rings (Target - Integer): The target variable representing the number of rings on the abalone. Age can be estimated by adding 1.5 to this value. This is the variable we aim to predict in the regression problem. Conducted Regression model provided below. Residuals: Min 1Q -23.3170 -1.6026 (Intercept) Sex Infant Sex Male Length Height Sex Infant*Length Sex Male Length Median -0.5675 3Q 0.8234 Estimate 6.12 -4.87 -1.33 2.88 21.14 7.30 1.96 Max 16.9521 Std. Error 0.48 0.56 0.61 0.93 1.71 1.05 1.04 T-value 12.67 -8.68 -2.20 3.08 12.36 6.98 1.88 P-value <0.001 <0.001 0.03 0.00 <0.001 0.00 0.06 a) Write estimated regression model for each sex (M, F, I). (10 pt) b) Interpret average ages for each sex for fixed length and fixed height. (5 pt) c) Interpret age of male, female and infant abalones based on their lengths. (5 pt) d) Is there significant difference between intercepts and slopes of each sex? Show. (10 pt) Predict the age of abalones (in years) based on various physical features such as length and height. The target variable is the number of rings, where +1.5 gives the age in years. The aim is to build a regression model that can accurately estimate the age of abalones using these features. Sex (Categorical): Gender of the abalone, with M (Male), F (Female), and I (Infant). Length (Continuous): Denotes the longest shell measurement of the abalone in millimeters. Height (Continuous): Indicates the height of the abalone with meat in the shell, measured in millimeters. Rings (Target - Integer): The target variable representing the number of rings on the abalone. Age can be estimated by adding 1.5 to this value. This is the variable we aim to predict in the regression problem. Conducted Regression model provided below. Residuals: Min 1Q -23.3170 -1.6026 (Intercept) Sex Infant Sex Male Length Height Sex Infant*Length Sex Male Length Median -0.5675 3Q 0.8234 Estimate 6.12 -4.87 -1.33 2.88 21.14 7.30 1.96 Max 16.9521 Std. Error 0.48 0.56 0.61 0.93 1.71 1.05 1.04 T-value 12.67 -8.68 -2.20 3.08 12.36 6.98 1.88 P-value <0.001 <0.001 0.03 0.00 <0.001 0.00 0.06 a) Write estimated regression model for each sex (M, F, I). (10 pt) b) Interpret average ages for each sex for fixed length and fixed height. (5 pt) c) Interpret age of male, female and infant abalones based on their lengths. (5 pt) d) Is there significant difference between intercepts and slopes of each sex? Show. (10 pt)

Answer rating: 100% (QA)

Based on the regression model provided we will address each of the questions a to d As per your screenshot the regression model coefficients and their significance are presented which we will use to b... View the full answer