8. Refer to the accompanying data set and construct a 95% confidence interval estimate of the mean pulse rate of adult females; then do the same for adult males. Compare the results. 1 Click the icon to view the pulse rates for adult females and adult males. Pulse Rates (beats per minute) Construct a 95% confidence interval of the mean pulse rate for adult females. Males Females 81 72 80 bpm < u< (Round to one decimal place as needed.) bpm 82 76 63 97 84 51 73 57 70 Construct a 95% confidence interval of the mean pulse rate for adult males. 63 70 66 74 53 56 56 84 bpm <µ< (Round to one decimal place as needed.) bpm 59 64 81 89 53 55 75 91 Compare the results. 75 80 86 88 53 75 88 92 64 62 55 93 O A. The confidence intervals do not overlap, so it appears that adult females have a significantly higher mean pulse rate than adult males. 72 65 36 66 60 96 68 90 O B. The confidence intervals overlap, so it appears that there is no significant difference in mean 67 60 83 82 pulse rates between adult females and adult males. 79 68 77 79 C. The confidence intervals overlap, so it appears that adult males have a significantly higher mean pulse rate than adult females. 79 55 80 76 64 58 65 58 O D. The confidence intervals do not overlap, so it appears that there is no significant difference in 68 68 67 103 mean pulse rates between adult females and adult males. 94 71 77 76 42 86 63 77 84 61 63 76
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.
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