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Homework statistics

Title: Homework statistics chapter 7

Name: Date:

Introduction: The most usual applications of Statistics is describing a set of data descriptive statistics, regression, and hypothesis testing and inferential statistics. The two main branches are descriptive and inferential statistics. People who do not have any formal training in statistics are more familiar with inferential statistics than with descriptive statistics. Descriptive Statistics Definition

The descriptive statistics is the type of statistical analysis which helps to describes about the data in some meaningful way. The statistics is used to describe quantitatively about the important features of the data or information. The descriptive statistics gives the summaries of the given sample as well as the observations done. These summaries or descriptions can either be graphical or quantitative. Inferential Statistics Definition

Inferential statistics is the type of statistics which deals with making conclusions. It inferences about the predictions for the population. It also analyses the sample. Basically, the inferential statistics is the procedure of drawing predictions and conclusions about the given data which is subjected to the random variations. Inferential statistics includes detection and prediction of observational and sampling errors. This type of statistics is being utilized in order to make estimates and test the hypotheses using given data. There are two major divisions of inferential statistics: 1) Confidence Interval: The confidence interval is represented in the form of an interval that provides a range for the parameter of given population. 2) Hypothesis Test: Hypothesis tests are also known as tests of significance which tests some claim for the population by analyzing sample. In this paper we will focus on hypothesis to test the raw and make a logical conclusion for example The Framingham Heart Study dataset provided, using ANOVA multivariable linear regression analysis using BMI as a continuous variable. Before we conduct the regression and ANOVA test I would like to make the hypothesis. Null Hypothesis H0: The BMI is not related to the patient characteristics in the Framingham Heart Study. Alternative Hypothesis H1: The BMI is related to the patient characteristics in the Framingham Heart Study. Level of significance is 0.05 Before using excel I excluded participants with missing data on analysis variables (age, sex, systolic blood pressure, total serum cholesterol, current smoker, and diabetes = cleaning the data). To perform simple linear regression (ANOVA) I assumed BMI as independent variable and independent variables are age, sex, systolic blood pressure, total serum cholesterol, current smoker, and diabetes and SEX is coded 1=male and 2=female. The output of the regression:

From the regression output we can see that the significance F value is 0.500 which is less than 0.05, we reject Null hypothesis and concluded that the BMI is related to the patient characteristics in the Framingham Heart Study. The R-squared (R2) is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. For this model the R square is 0.000 which indicates that there is 0% variance for a dependent variable that's explained by an independent variable or variables in a regression model. The significance of individual variable is given below.

The individual variable is significance and concluded that for individual variable The BMI is related to the patient characteristics in the Framingham Heart Study. References

Walpole, R. (1982). Introduction to Statistics. (3rd ed.). Prentice Hall Publication. Downie, N. M. & Heath, R. W. (1965). Basic Statistical Methods (2nd ed.). Harper & Row Publisher

Reid, H. (2013, August). Introduction to Statistics. SAGE Publication.

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2019/ 10/ 3 Originality Report

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The most usual applications of Statistics is describing a set of data descriptive statistics, regression, and hypothesis testing and inferential statistics. The two main branches are descriptive and inferential statistics. People who do not have any formal training in statistics are more familiar with inferential statistics than with descriptive statistics. Descriptive Statistics Definition

Original source

The most usual applications of Statistics is describing a set of data descriptive statistics hypothesis testing and inferential statistics The two main branches are descriptive and inferential statistics People who do not have any formal training in statistics are more familiar with inferential statistics than with descriptive statistics Descriptive Statistics Definition

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Student paper

The descriptive statistics is the type of statistical analysis which helps to describes about the data in some meaningful way. The statistics is used to describe quantitatively about the important features of the data or information. The descriptive statistics gives the summaries of the given sample as well as the observations done. These summaries or descriptions can either be graphical or quantitative.

Original source

The descriptive statistics is the type of statistical analysis which helps to describes about the data in some meaningful way The statistics is used to describe quantitatively about the important features of the data or information The descriptive statistics gives the summaries of the given sample as well as the observations done These summaries or descriptions can either be graphical or quantitative

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Inferential Statistics Definition

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Inferential Statistics Definition

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Inferential statistics is the type of statistics which deals with making conclusions. It inferences about the predictions for the population.

Original source

Inferential statistics Inferential statistics is the type of statistics which deals with making conclusions It inferences about the predictions of the population

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Student paper

It also analyses the sample. Basically, the inferential statistics is the procedure of drawing predictions and conclusions about the given data which is subjected to the random variations. Inferential statistics includes detection and prediction of observational and sampling errors. This type of statistics is being utilized in order to make estimates and test the hypotheses using given data.

Original source

It also analyses the sample Basically, the inferential statistics is the procedure of drawing predictions and conclusions about the given data which is subjected to the random variations Inferential statistics includes detection and prediction of observational and sampling errors This type of statistics is being utilized in order to make estimates and test the hypotheses using given data

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There are two major divisions of inferential statistics:

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There are two major divisions of inferential statistics

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The confidence interval is represented in the form of an interval that provides a range for the parameter of given population.

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it is represented in the form of an interval that provides a range for the parameter of given population

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Null Hypothesis H0:

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null hypothesis, H0

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Alternative Hypothesis H1:

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— alternative h., H1

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Introduction to Statistics.

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Introduction to Statistics

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Prentice Hall Publication.

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Prentice Hall Publication

2019/ 10/ 3 Originality Report

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Basic Statistical Methods (2nd ed.). Harper & Row Publisher

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Basic Statistical Methods (2nd ed.) Harper & Row Publisher

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Introduction to Statistics.

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Introduction to Statistics