This blog post covers an Introduction to Excel’ Data Analysis Tool, from installing and loading the Data Analysis ToolPak in Excel to the different Analyses applicable for Senior High School.
Getting Started with the Excel’ Data Analysis Tool
Load the Data Analysis ToolPak in Excel
Step 1: Under the File tab, click on Options.
Step 2: Click the Add-Ins category, and then in the Manage box, select Excel Add-ins and click Go.
Step 3: In the Add-Ins box, check the Analysis ToolPak check box, and then click OK.
- If Analysis ToolPak is not listed in the Add-Ins available box, click Browse to locate it.
- If you are prompted that the Analysis ToolPak is not currently installed on your computer, click Yes to install it.
After you load the Data Analysis ToolPak, the Data Analysis command is available in the Analysis group on the Data tab.
Descriptive statistics aims to show or summarize a given data set, which can be either an entire population or a sample of it. It is great for simplifying a large amount of data in a sensible way. It provides information about the central tendency and variability of your data.
Descriptive statistics are broken down into measures of central tendency and measures of variability or dispersion. Measures of central tendency include the mean, median, and mode, while measures of variability include variance, standard deviation, minimum and maximum values of the variables, kurtosis, and skewness.
A histogram is a commonly used graph to display the underlying frequency distribution, it used to summarize discrete or continuous data, and shows how often each different value in a set of data occurs. It is a tool used to calculates individual and cumulative frequencies for a cell range of data and data bins.
Histograms can display and provides a visual and graphical representation of a large amount of data, it is used to show how many of a certain type of value or variable occurs within a specific range in a data set.
t-Test: Paired Two Sample For Means
The t-Test Paired Two Sample for Means is an analysis tool that performs a paired two-sample Student’s t-Test to determine if the null hypothesis with equal population means can be accepted or rejected. This Paired t-Test is commonly used when there is a natural pairing of observations in the samples, such as when a sample individual or group is tested twice before and after some treatment. Paired t-Test can only compare the means for two related units on a continuous outcome that is normally distributed.
t-Test: Two-Sample Assuming Equal Variances
The t-Test Two-Sample Assuming Equal Variance or the Homoscedastic t-Test analysis tool perform a two-sample student’s t-Test assuming that both data sets came from distributions with the same variances or assumed to be equal.
t-Test: Two-Sample Assuming Unequal Variance
The t-Test Two-Sample Assuming Unequal Variance or the Heteroscedastic t-Test analysis tool perform a two-sample student’s t-Test assuming that the two data sets came from distributions with unequal variances. Heteroscedastic t-Test is used when there are distinct subjects in the two samples.
ANOVA: Single Factor
The single factor or one-way analysis of variance (ANOVA) is used to compare and determine the means between two or more independent or unrelated groups of values whether there is statistical evidence that the associated population means are significantly different. . This ANOVA Single Factor is used to test the null hypothesis that the means of several populations are all equal.
The Correlation analysis tool is used to investigate the relationship between two quantitative and continuous variables. It is particularly useful when there are more than two measurement variables for each of N subjects and to analyze the relationship between variables. The most common correlation coefficient is the Pearson Correlation Coefficient that is denoted by r, it is a measure of the strength of the association between the two variables.
The Regression analysis is a set of statistical methods used for indicating and estimating the significant relationships between a dependent variable or the outcome variable and one or more independent variables or the predictors or features. The most common form of regression analysis is linear regression where it includes several variations, such as linear, nonlinear, and multiple linear.