Use of microsoft excel in statistics

Using Excel for Statistical Analysis

MS Excel is one of the most commonly used tools for data analysis. The convenience of use and cost are two very important reasons why most data professionals prefer using Excel for statistical data analysis. However, using Excel for statistical analysis requires clarity of thought, data analysis knowledge, and strong decision-making skills.

Whether you are performing statistical analysis using Excel 2010 or Excel 2013, you need to have a clear understanding of charts and pivot tables. Most data analysts using Excel for statistical analysis depend largely on these two Excel features. Having knowledge of the essential statistics for data analysis using Excel answers is a plus.

Remember to install Data Analysis ToolPak if you are using Excel for statistical data analysis. In this discussion, we explain in detail the essential statistics for data analysis using Excel and how to perform descriptive analysis using Excel.

In this blog, I have tried to explore the functionalities of MS-Excel as a potential tool for statistical analysis and suggested some simple tricks and techniques that will save both time and energy.

Using Excel for Statistical Analysis: Pivot Tables

A PivotTable is an Excel tool for summarizing a list into a simple format. It helps you analyze all the data in your worksheet so as to make better business decisions. Excel can help you by recommending, and then, automatically creating PivotTables, which are a great way to summarize, analyze, explore, and present your data.

A pivot table may be used as an interactive data summarization tool to automatically condense large datasets into a separate, concise table. You can use it to create an informative summary of a large dataset or make regional comparisons between brand sales.

You can create PivotTables from lists, as you define which fields should be arranged in columns, which fields should become rows, and what data you wish to summarize.

Using Excel for Statistical Analysis: Descriptive Statistics

Descriptive Statistics tool in the Data Analysis add-in can be used on an existing data set to get up to 16 different descriptive statistics, without having to enter a single function on the worksheet. Descriptive Statistics gives you a general idea of trends in your data including:

• The mean, mode, median and range
• Variance and standard deviation
• Skewness
• Sample Variance
• Kurtosis and Skewness
• Count, maximum and minimum

Descriptive Statistics is useful because it allows you to take a large amount of data and summarize it. For example, you may want to represent the incomes of a community. Instead of showing it on an excel, you may summarize it, it becomes useful: an average wage, or a median income, is much easier to understand and then analyze the data.

You can find descriptive analysis by going to Excel→ Data→ Data Analysis → Descriptive statistics. It is the most basic set of analysis that can be performed on any data set.

Using Excel for Statistical Analysis: ANOVA (Analysis of Variance)

Analysis of variance (ANOVA) is a statistical technique that is used to check if the means of two or more groups are significantly different from each other. ANOVA checks the impact of one or more factors by comparing the means of different samples.

ANOVA method in Excel shows whether the mean of two or more data set is significantly different from each other or not. In other words, ANOVA analyses two or more groups simultaneously and finds out whether any relationship is there among the groups of data set or not.

For example, you may use ANOVA if you want to analyze the traffic of three different cities and find out which one is more efficient in handling the traffic (or if there are no significant differences among the traffic).

You will find three types of ANOVA in Excel:

• ANOVA single factor
• ANOVA two factor with replication
• ANOVA two factor without replication

If you have three groups of datasets and want to check whether there is any significant difference between these groups or not, you can use ANOVA single factor. If the P-value in the ANOVA summary table is greater than 0.05, you can say that there is a significant difference between the groups.

Using Excel for Statistical Analysis: Moving Average

Moving Average, another great tool for those using Excel for statistical analysis, is ideal for time series data such as stock price, weather report, attendance in class, etc. Moving Average is used extensively in stock price as a technical indicator. If you want to predict the stock price of today, the last ten days’ data would be more relevant than the last 1 year.

You may, simply plot the moving average of the stock having a 10-day period and then predict the estimated price. The same rule may be applied for predicting the temperature of a city. The recent temperature of a city can be calculated by taking the average of the last few weeks rather than the last few months.

Using Excel for Statistical Analysis: Rank and Percentile

The Rank and Percentile, another popular Excel features used for data analysis, is useful for finding the rank of all the values in a list. The best part of using the Rank and Percentile feature is that the percentile is also added to the output table.

The percentile is a percentage that indicates the proportion of the list which is below a given number. It calculates the ranking and percentile in the data set. For example, if you are managing a business of several products and want to find out which product is contributing to a higher revenue, you can use this rank method in Excel.

In the left table, we have our data on the revenues of different products. And we want to rank this data of products based on their revenue. With the help of rank and percentile, we can get the table shown on the right. You can observe that now the data is sorted and respective rank is also marked with each data.

Percentile shows the category in which the data belongs, such as the top 50%, top 30%, etc. In the summary table, the rank of product 7 is 4. As the total number of data is 7, we can easily say that it belongs to the top 50% of the data.

Using Excel for Statistical Analysis: Regression

Regression is one of the best features in Excel. It is widely used for using Excel for statistical data analysis. Regression is a process of establishing a relationship among many variables; to establish a relationship between dependent variables and independent variables.

Regression is great for use for using Excel for statistical data analysis. You, may, for example, want to see if there is an increase in the revenue of the product, which is not due to the increase in the advertisement.

If you performing statistical analysis using Excel 2010, Regression Analysis is the best way of mathematically sorting out which of those variables does indeed have an impact. It answers the questions: Which factors matter most? Which can we ignore? How do those factors interact with each other? And, perhaps most importantly, how certain are we about all of these factors?

These factors are more commonly known as variables. You may have your dependent or independent variables. In order to conduct a regression analysis, you gather the data on the variables in question.

You may take all of your monthly sales numbers, the past five years and any data on the independent variables you may find useful. You may, for example, find out the average monthly rainfall for the past five years as well.

Using Excel for Statistical Analysis: Random Number Generator

If you are using Excel for statistical data analysis, on a regular basis, Random Number Generator must be your top choice for generating a series of random numbers. This simple function in Excel gives you more flexibility in the random number generation process. It gives you more control over the generated data.

A random number is one that is drawn from a set of possible values, each of which is equally probable. In statistics, this is called a uniform distribution, because the distribution of probabilities for each number is uniform (i.e., the same) across the range of possible values.

For example, a good (unloaded) die has the probability 1/6 of rolling a one, 1/6 of rolling a two and so on. Hence, the probability of each of the six numbers coming up is exactly the same, so we say any roll of our die has a uniform distribution.

When discussing a sequence of random numbers, each number drawn must be statistically independent of the others. This means that drawing one value doesn’t make that value less likely to occur again. This is exactly the case with our unloaded die: If you roll a six, that doesn’t mean the chance of rolling another six changes.

Two very essential statistics for data analysis using Excel:

• The function RANDBETWEEN returns a random integer number
• The function RAND () returns a random real number of a uniform distribution. It will be less than 1 and greater than or equal to 0.

Using Excel for Statistical Analysis: Sampling

Sampling is one of the most readily preferred Excel tools if you are using Excel for statistical data analysis. This option is used for creating samples from a huge population. You can randomly select data from the dataset or select every nth item from the set.

For example, if you may want to measure the effectiveness of female call center employee in a call center, you can use this tool to randomly select few data every month and listen to their recorded calls and give a rating based on the selected call.

Sampling Methods:

If you are using statistical analysis using Excel 2010, you can make use of two sampling methods in Excel for retrieving or identifying items in your data set:

• Periodic: In this case, you specify the Period n at which you want sampling to take place. The nth value in the input range and every nth value thereafter is copied to the output column. Sampling stops when the end of the input range is reached.
• Random: In this case, you specify the Random Number of Samples. This number of values is drawn from random positions in the input range. A value can be selected more than once. (I.e. sampling is with replacement).

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Excel is one of the most dynamic and intriguing tools for statistical analysis. We have discussed a few features. Play around with Excel to know more about other tools and techniques. You may also look for essential statistics for data analysis using Excel answers.

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Descriptive statistics with excel is a popular way to describe your data. It makes the tons of formula became easier just by simple click and drop.

Years ago, I do the formula one by one and find the statistic value that I need. It is really uncomfortable, is not it?

You have to remember the correct formula of your data and choose the right formula because perhaps there is more than one similar formula for one statistic value.

Look at he picture I present below. This is what you will using descriptive statistics with excel by typing the formula one by one

There are 7 formulas that excel provide to you to generate the variance of your data. My question is, which one will you use?

Still, writing one by one formula to summarize the statistic value that you need? Well, keep reading!

Microsoft Excel is a phenomenal software developed by Microsoft helping Billions of human to solve their problems. Excel helps us to makes almost our calculation problem easier included statistics.

Why using descriptive statistics in excel?

If you are wondering why you should Microsoft Excel to process your statistical data, let me tell you these interesting facts!

1. The user interface is so friendly

Yes, Microsoft Excel interface is so friendly so almost every user could use it without any meaningful problem. You may use it through simple steps and clicks to produce the output that you want.

2. No coding needed

Usually, almost all statistical software needs to code the form. But, Excel does not require you to code anything. You just need to know the simple formula or use the toolbar that will help you to finish the job.

3. Easy to interpret

The output is served simply so we may see it and understand what the output is. If you did not strong statistical basics, do not worry. It’s just basic formula which you may learn in your study.

Before using Microsoft Excel to process your data, you must activate the data analysis toolpak to makes your job easier.

Follow these simple steps!

1. Activate the data analysis toolpak, go to file >> options

2. Choose add ins >> analysis toolpak

3. Ok

Now, you will have the tools that you need to make your works easier and faster. By using this toolpak, you do not have to input every single formula that you need. Now, let’s calculate the descriptive statistics in excel

Already have your data set? Let’s do the analysis. Here is the steps!

1. Go to Data >> data analysis

2. You’ll see many statistical options there, choose descriptive statistics >> ok

3. In the popup window, you have several fields that you have to fill

• Input range: block the data you want to analyze
• Grouped by: whether the data is grouped in columns or rows
• Labels in the first row: if the blocked data has labels in the first row, check this
• Output options: where the output will be displayed
• Summary statistics: if you want to do descriptive statistics analysis
• The confidence level for mean: if you want to show confidence level for mean
• Kth largest: if you want to show the data in “k”th largest
• Kth smallest: if you want to show the data in “k”th smallest

4. Click Ok

5. See the magic happens!

Interpretation of Descriptive Statistics Output in Excel

1. Mean = 7,434. In average, there are 7,434 poor people in these 12 areas

2. Standard error = 468.412. This value indicates that the sample we chose has a fairly high distribution of the population mean.

3. Median = 7,575. This value indicates that the middle numbers of poor people based on the sample we use are 7,575 people.

4. Mode = 8000. This value shows that the most number of poor people based on the sample we have is 8000 people.

5. Standard deviation = 1622. This value indicates that the sample values ​​that we use are spread far enough from the mean value.

6. Kurtosis = -0.68485. Because the value of kurtosis is smaller than 3, we can conclude that the sample used is platicurtic distribution (tends to be flat).

7. Skewness = -0.12018. Because the skewness value is smaller than zero, we can conclude that the data tends to be left inclined or left skewed.

8. Range = 5100. This value indicates that the difference between the regions with the highest number of poor people and the lowest number of poor people is 5100 people.

9. Minimum = 4900. This value shows the lowest number of poor people is 4900 people in the L area.

10. Maximum = 10,000. This value shows that the highest number of poor people is 10,000 people in J area.

11. Sum = 89,210. This value indicates that the total number of poor people based on the data used was 89,210 people.

12. Count = 12. This value indicates the amount of data used is 12.

13. Confidence level = 1030,968. It’s quite difficult to understand, right? Okay, keep reading.

Confidence interval means we will predict a value in the form of a range. In this case, we need upper values ​​and lower values.

In the descriptive statistics feature in Microsoft Excel, they only provide one value, and this figure is very far from the mean.

The confidence level value that appears is a value that can be used to get the upper and lower limits of the confidence interval you are using.

If you want to get the upper limit, you simply add an average to the value of the confidence level. The following calculations. Check the picture below!

If you want to get a lower bound value, you can simply reduce the average value with that confidence level. Consider the following picture.

Now, let’s make the interpretation of this value!

With a confidence level of 95 percent, the average number of poor people in the 12 regions is 6,403 to 8,465 people.

The Disadvantage of Using Excel For Descriptive Statistics

1. The formula is limited

Although the formula is super easy just by drag and click, the numbers of excel formula in the statistical process are limited.

Excel provided not much formula so the user can use to do data processing. But, Excel is the best tool to study statistical computing if you want to be advanced in the future.

2. It is only for numerical data

It’s sad but if you are using categorical or non-numeric data, probably excel is not for your research. Excel only read the data in numeric format. Even you transform it into numerical form, it’s quite difficult to read the output.

3. It is only for single variable analysis

Conclusion

Overall, the steps of using descriptive statistics in excel are:

1. Prepare the data set.

2. Activate analysis toolpak add-ins add options menu.

3. Choose the descriptive statistics at the data analysis menu.

4. Check the statistic value that you want to generate.

5. Click Ok.

6. Do not forget to make the output interpretation.

If you want to do more advanced analysis by software, I recommend you to check the descriptive statistics on spss article. You will find an easier way to produce the descriptive statistics even for the numerical or categorical data set.

In this tutorial, I will show you how to use the Data Analysis ToolPak to return some descriptive statistics about your data in Microsoft Excel. This includes the mean, standard error, median, mode and so much more.

How to install the Data Analysis ToolPak

A useful feature in Excel that many people do not know about is the Data Analysis ToolPak.

The Data Analysis ToolPak is an add-on created by Microsoft to make it easier to perform different data analysis procedures.

To ensure you have the Data Analysis ToolPak activated correctly, go to File>Options.

Then select Add-ins.

At the bottom, where it says Manage, ensure you select Excel Add-ins and click Go.

Ensure you have the Analysis ToolPak option ticked, and click the OK button.

Now, when you select the Data tab at the top, you should see a Data Analysis button appears.

Now we’re ready to perform the descriptive statistics.

To perform descriptive statistics in Excel, go to Data>Data Analysis.

Then, from the list, select Descriptive Statistics.

This should open a new window.

For the input range, this is where you enter the range of cells containing your data.

Next, need to tell Excel how your data are entered in your sheet.

If you have the values stacked in a column, then these will be grouped by columns. If they were entered into rows instead, then select the rows option.

If you also have labels at the top of your data in the first row, then ensure you select Labels in First Row.

For the output options, this is where you specify where you want the descriptive statistics to be returned

There are three options:

• Output Range – Enter a specific cell in this same worksheet where you want the results to be entered
• New Worksheet Ply – Enter the results in a new worksheet
• New Workbook – Enter the descriptive statistics in an entirely new Excel file

Underneath, you can tick the various descriptive statistic options that you want to perform. At a minimum, you will want to select the Summary Statistics. The Confidence Level for Mean, Kth Largest and Kth Smallest options are optional, and I will explain more about these later.

So that’s the setup done, now press the OK button to perform the descriptive statistics.

How to interpret the descriptive statistics results

Below are some descriptive statistics I have based on some example data. I’ll now go over and explain each output.

Mean

The first result is the mean, or average, value.

This is where you add all the values up in your sample and divide by the number of values in the sample.

To calculate the mean value youself, you can use the AVERAGE function.

=AVERAGE(data)

Standard error

The standard error is a measure of the variability of sample means in a sampling distribution of means.

The higher the standard error, the higher the variability.

You can calculate the standard error yourself by taking the standard deviation and dividing it by the square root of the count.

=STDEV.S(data)/SQRT(COUNT(data))

Median

Say we sorted our data in ascending order from smallest to largest. The median will be the number that lies in the middle of the values, in my case, the answer is 50.3.

You can calculate the median separately by using the MEDIAN function.

=MEDIAN(data)

Mode

The mode represents the value that appears the most in the sample

The mode for my example is 49.8 since this value appears twice.

If you want to calculate this yourself, you can use the MODE.SNGL function.

=MODE.SNGL(data)

Standard deviation

The standard deviation is a measure of the amount of variation in the data relative to the mean, where the higher the standard deviation, the higher the variability.

If you would like to calculate the standard deviation of a sample separately from the descriptive statistics, then use the STDEV.S function.

=STDEV.S(data)

Variance

The variance is simply the square of the standard deviation.

The Excel function for the variance of a sample is VAR.S.

=VAR.S(data)

Kurtosis

Kurtosis is a measure that defines how heavily the tails of a distribution differ from the tails of a normal distribution.

In Excel, a normal distribution has a kurtosis value of 0, positive values indicate a relatively peaked distribution and negative values indicate a relatively flat distribution.

You can use the KURT function to calculate the kurtosis separately.

=KURT(data)

Skewness

Skewness is a measure of asymmetry of the data distribution.

A value of 0 indicates a perfectly symmetrical distribution.

If the value is between -1 and 1, but isn’t 0, then the data are considered fairly skewed. If the value is outside this range, then the data are highly skewed.

The function for skewness is SKEW.

=SKEW(data)

Range

The range is simply the difference between the smallest and largest values in the data.

Minimum

The minimum is the smallest number in the data.

You can use the MIN function to work this out yourself.

=MIN(data)

Maximum

As the name suggests, the maximum is the largest number in the data.

To calculate this separately, use the MAX function.

=MAX(data)

SUM

The sum is the total calculated when you add up all the values in the data.

Use the SUM function to do this yourself.

=SUM(data)

Count

The count is the total number of values you have in your data.

Use the COUNT function to calculate the count separately.

=COUNT(data)

Kth largest

During the descriptive statistics setup, if you selected the Kth Largest option, then you’ll also have this result too.

Say you have 1 in the box next to the Kth Largest option, this would mean the first largest value in the data will be returned – in other words, the maximum.

If a 2 was used instead, then the second largest value will be returned, and so on and so forth.

Kth smallest

Similar to the above, if you selected the Kth Smallest option during the setup, then you’ll also have this result returned.

Having 1 entered during the setup will mean the first smallest value will be determined – the minimum value.

Again, changing this input to a 2 would mean the second smallest value is returned instead.

Confidence level

The last entry in the descriptive statistics table is the confidence interval value.

This is the value you can add and subtract from the mean to calculate the upper and lower confidence intervals, respectively. Specifically, these will be the 95% confidence intervals, since 95 was entered during the setup.

If you want to learn more about calculating confidence intervals in Microsoft Excel, then check out this post.

How to perform descriptive statistics in Excel: Final words

You should now know how to calculate some descriptive statistics in Excel.

Using the Data Analysis ToolPak makes this process so much easier and quicker than using separate functions.

Microsoft Excel version used: 365 ProPlus