For Microsoft excel you just install your Microsoft office and open up Excel then you can easily use data analysis. But if you are a Mac user, then you need to go through extra steps. You need a data analysis tool. So here you will know how to add data analysis tool Pak in excel on a mac. First of all, go to your excel spreadsheet and open up. How to do Simple Linear Regression in Excel 2016 for Mac (Commute Time Example) with scatterplots.
As the article suggests, today we will learn how to add Analysis ToolPak in Excel For Mac and Windows PCs. So let's get started.
The Analysis ToolPak is an add-on in Microsoft Excel. By default, this add-in is not installed in Excel.
Analysis toolPak of excel contains very helpful tools for statistical, financial, and engineering data analysis.
How to Add Analysis ToolPak to Excel 2016 in Windows?
To install the Analysis toolPak in Excel 2016 follow these steps.
- Click on file.
- Click on option. You’ll see the excel options dialogue box.
- Click on Add-Ins. It is on the left, second option from the bottom.
- Locate Analysis ToolPack and click on it.
- In the bottom, you must be able to see manage drop down. Select Excel-Add-ins and click on Go button.
- All available Add-ins will be shown to you. Select Analysis Toolpak and click OK.
Analysis ToolPak is added now. You can see it in the Data tab in the rightmost corner.
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How to Add Analysis ToolPak in Excel 2016 for Mac?
To install Analysis toolpak in Excel for Mac, follow below steps.
- Goto tools or Insert menu.
- Locate Add-Ins and Click on it.
- You’ll be prompted to choose from available add-ins. Select Analysis Toolpak.
- Click Ok.
Analysis Toolpak is added. Goto data tab. In the rightmost corner you’ll find Analysis Toolpak waiting for you.
Analysis Toolpak has a number of useful statical tools that we will explore in our that we have explored in analysis tutorials.
The Analysis ToolPak in Excel 2010
Steps for a user to manually install Analysis ToolPak in Excel:
- Click on File ribbon
- Click on Options
- From Options, select Add-Ins
- Select Analysis ToolPak and click on Go button
- You can see 4 Add-Ins options are available, check the Analysis ToolPak
- Click on OK button
- When you check Data ribbon, this time you will see Data Analysis option appears in Analysis Group.
- When you click on Data Analysis, you will find the dialog box as shown below:
There are a total of 19 options available in the Data Analysis:
1. Anova: Single Factor
2. Anova: Two-Factor with Replication
3. Anova: Two-Factor without Replication
4. Correlation
5. Covariance
6. Descriptive Statistics
7. Exponential Smoothing
8. F-Test Two Sample for Variance
9. Fourier Analysis
10. Histogram
11. Moving Average
12. Random Number Generation
13. Rank and Percentile
14. Regression
15. Sampling
16. t-Test: Paired Two Sample for Means
17. t-Test: Two-Sample Assuming Equal Variances
18. t-Test: Two-Sample Assuming Unequal Variances
19. Z-Test: Two-Samples for Mean
An add-in is simply a hidden tool that adds new features in Microsoft Excel 2010 and 2013.
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R Square | Significance F and P-Values | Coefficients | Residuals
This example teaches you how to run a linear regression analysis in Excel and how to interpret the Summary Output.
Below you can find our data. The big question is: is there a relation between Quantity Sold (Output) and Price and Advertising (Input). In other words: can we predict Quantity Sold if we know Price and Advertising?
1. On the Data tab, in the Analysis group, click Data Analysis.
Note: can't find the Data Analysis button? Click here to load the Analysis ToolPak add-in.
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2. Select Regression and click OK.
3. Select the Y Range (A1:A8). This is the predictor variable (also called dependent variable).
4. Select the X Range(B1:C8). These are the explanatory variables (also called independent variables). These columns must be adjacent to each other.
5. Check Labels.
6. Click in the Output Range box and select cell A11.
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7. Check Residuals.
8. Click OK.
Excel produces the following Summary Output (rounded to 3 decimal places).
R Square
R Square equals 0.962, which is a very good fit. 96% of the variation in Quantity Sold is explained by the independent variables Price and Advertising. The closer to 1, the better the regression line (read on) fits the data.
Significance F and P-values
To check if your results are reliable (statistically significant), look at Significance F (0.001). If this value is less than 0.05, you're OK. If Significance F is greater than 0.05, it's probably better to stop using this set of independent variables. Delete a variable with a high P-value (greater than 0.05) and rerun the regression until Significance F drops below 0.05.
Most or all P-values should be below below 0.05. In our example this is the case. (0.000, 0.001 and 0.005).
Coefficients
The regression line is: y = Quantity Sold = 8536.214-835.722 * Price + 0.592 * Advertising. In other words, for each unit increase in price, Quantity Sold decreases with 835.722 units. For each unit increase in Advertising, Quantity Sold increases with 0.592 units. This is valuable information.
You can also use these coefficients to do a forecast. For example, if price equals $4 and Advertising equals $3000, you might be able to achieve a Quantity Sold of 8536.214 -835.722 * 4 + 0.592 * 3000 = 6970.
Residuals
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The residuals show you how far away the actual data points are fom the predicted data points (using the equation). For example, the first data point equals 8500. Using the equation, the predicted data point equals 8536.214 -835.722 * 2 + 0.592 * 2800 = 8523.009, giving a residual of 8500 - 8523.009 = -23.009.
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You can also create a scatter plot of these residuals.