______ is a generalization of linear regression for predicting a categorical out
ID: 3132666 • Letter: #
Question
______ is a generalization of linear regression for predicting a categorical outcome variable. A. Cluster analysis B. Multiple linear regression C. Logistic regression D. Discriminat analysis Fitting a model too closely to sample data, resulting in a model that dose not accurately reflect the population is termed as _____ A. Approximation. B. Postulating C. Hypothesizing. D. Overfitting. A time series with seasonal pattern can be modeled by treating the season as a _____ A. Categorical variable B. Dummy variable C. Predictor variable D. Dependent variable ____ is the process of estimating the value of a categorical outcome variable. A. Classification B. Prediction C. Sampling D. Validation A ____ classifies a categorical outcome variable by splitting observations into groups via a sequence of historical rules. A. Regression line B. Classification tree C. Scatter chart. D. Classification confusion matrix______ is a generalization of linear regression for predicting a categorical outcome variable. A. Cluster analysis B. Multiple linear regression C. Logistic regression D. Discriminat analysis Fitting a model too closely to sample data, resulting in a model that dose not accurately reflect the population is termed as _____ A. Approximation. B. Postulating C. Hypothesizing. D. Overfitting. A time series with seasonal pattern can be modeled by treating the season as a _____ A. Categorical variable B. Dummy variable C. Predictor variable D. Dependent variable ____ is the process of estimating the value of a categorical outcome variable. A. Classification B. Prediction C. Sampling D. Validation A ____ classifies a categorical outcome variable by splitting observations into groups via a sequence of historical rules. A. Regression line B. Classification tree C. Scatter chart. D. Classification confusion matrix
A. Cluster analysis B. Multiple linear regression C. Logistic regression D. Discriminat analysis Fitting a model too closely to sample data, resulting in a model that dose not accurately reflect the population is termed as _____ A. Approximation. B. Postulating C. Hypothesizing. D. Overfitting. A time series with seasonal pattern can be modeled by treating the season as a _____ A. Categorical variable B. Dummy variable C. Predictor variable D. Dependent variable ____ is the process of estimating the value of a categorical outcome variable. A. Classification B. Prediction C. Sampling D. Validation A ____ classifies a categorical outcome variable by splitting observations into groups via a sequence of historical rules. A. Regression line B. Classification tree C. Scatter chart. D. Classification confusion matrix
Explanation / Answer
Answer to question# 1)
In linear regression the dependent variable is contnuous and has infinite values
but when the output variable has some limited values , or categories the linear regession is generalised as Logistic regression
thus the correct answer for this question is : C. logistic regression
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