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ADVANCED STATISTICAL ANALYSIS, NAME:PELSI SANTIKA NIM:A2021029 CLASS:B -…
ADVANCED STATISTICAL ANALYSIS
ANOVA (ANALYSIS OF VARIANCE)
TYPES
One-Way ANOVA
Consists of only one factor at a time
Example : The influence of the learning model on learning achievement.
If the various experimental groups differ in terms of only one factor at a time- a one way ANOVA is used
Two-Way ANOVA
Example : the effect of learning models and initial abilities on learning achievement
Consist of two or more factors at a time
If the various groups differ in terms of two or more factors at a time, then a two way ANOVA is performed
EXAMPLE
A study wants to test whether there is a difference in the average weight gain of children aged 3-5 years who take certain vitamins.
DEFINITION
ANOVA (Analysis of Variance) is statistical analysis method that belongs to the branch of statistical inferential.
ANOVA was used to test the difference in the mean data for more than 2 groups.
CORRELATION
DEFINITION
A statistical method used to determine whether a relationship between variables exists
CORRELATION COEFFICENT
A measure that represents strength of the relationship between or among the variables.
Correlation coefficient formulas are used to find how strong a relationship is between data. The formulas return a value between -1 and 1, where:
1 indicates a strong positive relationship.
-1 indicates a strong negative relationship.
TYPES
Simple, Partial and Multiple Correlation
The correlation is said to be simple when only two variables are studied
The correlation is said to be Multiple when three variables are studied simultaneously
Partial correlation is a process in which we measure of the strength and also direction of a linear relationship between two continuous variables
Linear and Non-Linear (Curvilinear) Correlation
The correlation is said to be linear when the amount of change in one variable to the amount of change in another variable tends to bear a constant ratio
If a relationship between two variables is not linear, the rate of increase or decrease can change as one variable changes, causing a "curved pattern" in the data.
Positive and Negative Correlation
The correlation is positive when both the variables move in the same direction, i.e. when one variable increases the other on an average also increases and if one variable decreases the other also decreases.
The correlation is said to be negative when both the variables move in the opposite direction, i.e. when one variable increases the other decreases and vice versa.
EXAMPLE
Correlational Study of Teacher Preparation Program on Student Achievement
Correlation Study of the Relationship between Emotional Intelligence and Effective Leadership Practices
STATISTICS
HYPOTHESIS TESTING
Collect data
You collect data by administering an instrument or recording behaviors on a check sheet for participants.
Then, you code the data and input it into a computer file for analysis.
Compute the sample statistic.
After calculating the p value, we compare it with a value in a table located in the back of major statistics books related to the statistical test by finding the value given our significant level
Using a computer program, you compute a statistic, or p value.
A p value is the probability (p) that a result could have been produced by chance if the null hypothesis were true.
Set the level of significance, or alpha level, for rejecting the null hypothesis.
The significance level or alpha level is the probability of making the wrong decision when the null hypothesis is true.
Usually, these tests are run with an alpha level of .05 (5%), but other levels commonly used are .01 and .10.
Alpha levels are used in hypothesis tests.
Make a decision about rejecting or failing to reject (accept) the null hypothesis
If p value is greater than α, you accept the null hypothesis
If the p value is less than α, you reject the null hypothesis
Identifying your null and alterative hypothesis.
Null Hypothesis
There is no difference between smokers and nonsmokers on depression scores.
Alternative Hypothesis
(non-directional and directional)
There is a difference between smokers and nonsmokers on depression scores.
Smokers are more depressed than nonsmokers.
QUESTIONS TO ASK
How confident are you that your sample score is right?
This is the confidence interval approach.
A confidence interval or interval estimate is the range of upper and lower statistical values that is consistent with observed data and is likely to contain the actual population mean.
Does the sample score or differences between two groups make practical sense?
This is the effect size approach.
Effect size is a means for identifying the practical strength of the conclusions about group differences or about the relationship among variables in a quantitative study.
Is the sample score (e.g., the mean difference between two groups) probably a wrong estimate of the population mean?
The procedure you use to examine this question is hypothesis testing.
Hypothesis testing is a procedure for making decisions about results by comparing an observed value of a sample with a population value to determine if no difference or relationship exists between the values.
ESTIMATING USING CONFIDENCE INTERVALS
Confidence intervals help us decide how large the difference actually might be and to estimate a range of acceptable values.
A confidence interval or interval estimate is the range of upper and lower statistical values that are consistent with observed data and are likely to contain the actual population mean.
TYPES
DESCRIPTIVE STATISTICS
(BASIC STATISTICS )
Descriptive statistics help you analyze descriptive questions.
INFERENTIAL STATISTICS (ADVANCED STATISTICS)
When you compare groups or relate two or more variables, inferential analysis comes into play.
The basic idea is to look at scores from a sample and use the results to draw inferences or make predictions about the population.
DETERMINING EFFECT SIZE
Effect size identifies the strength of the conclusions about group differences or about the relationship among variables in a quantitative study.
The larger the effect size the stronger the relationship between two variables.
REGRESSION ANALYSIS
TYPES
Multiple Linear Regression
Using starting from two or more independent variables.
Multiple Linear Regression is a linear regression model involving more than one independent variable or predictor.
Logistics Regression
Determine the probability of an event.
In statistics is used to predict the probability of occurrence of an event by fitting the data to the logit function of the logistic curve.
This method is a general linear model used for binomial regression.
Logistic regression is a type of regression that connects one or several independent variables (independent variables) with the dependent variable in the form of categories; usually 0 and 1.
Simple Linier Regression
Consists of only one dependent variable and one independent variable.
Statistical method that serves to test the extent of the causal relationship between the Causing Factor Variable (X) and the Resulting Variable.
USES
It is one of the widely used tools in economic and business research where statistical interpretations are highly valued as their analysis is based more on cause and effect relationships.
It helps in predicting the dependent variable value from the independent variable values.
It helps in devising a functional relationship between two variables.
The coefficient of correlation and coefficient of determination can be established with the help of regression coefficients.
DEFINITION
Regression analysis is concerned with the study of the dependence of the variable, called the dependent variable, on one or the explaining variable with the aim of estimating or predicting the values of the dependent variable if the value of the explaining variable is known.
Regression Analysis is one of the most widely used tools in business analysis. It is the process of analyzing the relationship between variables.
In regression analysis, a regression equation will be determined and used to describe the pattern or function of the relationship that occurs between variables.
In multiple regression, more than two variables were studied.
(dependent variable) = y
(independent variable) = x
NAME:PELSI SANTIKA NIM:A2021029 CLASS:B