For my dissertation, my supervisor wants me to carry out descriptive statistics for some of my questions; I am trying to carry out descriptive statistics for one of my questions which contains different age categories (such as 18-20, 60+, and a prefer not to say option) but whenever I try to carry this out, I keep getting a message saying 'input range contains non-numeric data'. Why is this happening? I'm struggling to carry out descriptive statistics on SPSS so I want to do it on Excel instead.
@FrancesH1810 Sounds like the data in your Excel file isn't "clean": Numbers recognized as numbers for example, but rather seen/treated as text.
When you select the cells which should contain numbers, a small icon should appear. Click that icon it will allow you to change the numbers to numbers:
Descriptive Statistics In Excel
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I am trying to run descriptive stats on aggregate pre and posttest survey data. I highlighted the pretest data, and selected descriptive statistics. However, instead of providing descriptive stats on the entire survey, the results only give descriptives on each item in the survey.
I obtained the average for each survey item by highlighting item data, and selecting average. The calculation appears in the cell below each column of data. Then, I selected descriptive statistics, and highlighted the 'averages' in the row at the bottom of survey data. I selected 'row' in the descriptive statistics choices of column or row. This provided the descriptive stats for the entire survey...
Yea!!! Perhaps there are other ways of doing this...but if so, I couldn't figure it out. I'm used to working with SPSS, where you can just highlight all the survey data to obtain descriptive stats on group, multiple item surveys.
I hope this helps someone else...I didn't give the locations of where to find average, descriptive stats, etc., but thinking folks who need the answer to my question probably already know this. I'm just happy to have a relatively simple way to do this in Excel...
I am working on a data set with couple thousands of rows but with more than 300 columns/variable. Most of the variables are categorical and there some continuous. I need to get descriptive statistics including frequency for categorical and measures such as mean, median, std, etc for others. In any statistical packages such as SAS, SPSS, JMP you can just drag and drop as many variables as you want and in one run get the descriptive. Is there a way to accomplish the same in Power BI? any custom visualization are out there that can handle this? The only method that I can think of is to use either R or Python. Any thoughts/suggestions?
I have used Table.Profile function for most part. Now, the requirement somewhat changed and I need to get more stats on multiple variables.
For example, on continous variables suach age, total scores, etc I need to get N, Mean, Median, STDV, Min, Max, and IQR (Interquartile range),
and Box plot graph by let's say sites or clinics (See attached BoxPlot picture and formatting). For categorical, N, column % by let's say sites or clinics ((See attached Category example picture and formatting).
I can create these measures easily and format the report accordingly. However, I Have 80 differrent data sets with more than 1500 variables. Therefore, creatign these measures for 1500 variables would take forever and Table.Profile wouldn't give me all of the stats + Box Plot that I need. Any suggestions?
Im trying to replicate descriptive statistics (summary statistics) analysis tool in excel with python (jupyter notebook) by aggregating some of descriptive statistics availbale in pandas library, but everytime i add mode function in the code, it always return :
Note: to calculate the descriptive statistical values in this section, you must have enabled the Data Analysis Tools in Excel for Windows. This has already been done on the lab computers but if you are using a computer elsewhere, you may need to enable it. Go to the Excel Reference home page for instructions for PC and Mac.
To calculate descriptive statistics for a column of data, click on the Data ribbon. Click on Data Analysis in the Analysis section. Select Descriptive Statistics, then click OK. Click on the Input Range selection button, then select the range of cells for the column. (If you include a column label in your selection, check the "Labels in first row" checkbox.) Check the Summary Statistics checkbox. To put the results on the same sheet as the column of numbers, click on the Output Range radio button then click on the selection button. Click on the upper left cell of the area of the sheet where you would like for the results to go. Then press OK.
Drawing on his immense experience helping organizations gain value from statistical methods, Conrad Carlberg shows when and how to use Excel, when and how to use R instead, and how to use them together to get the best from both. Here he discusses how descriptive statistics tools in Excel and R can help you understand the distribution of the variables in your data set.
Regardless of the sort of analysis you have in mind for a particular data set, you want to understand the distribution of the variables in that set. The reasons vary from the mundane (someone entered an impossible value for a variable) to the technical (different sample sizes accompanying different variances).
Any of those events could happen, whether the source of the data is a sales ledger, a beautifully designed medical experiment or a study of political preferences. No matter what the cause, if your data set contains any unexpected values you want to know about it. Then you can take steps to correct data entered in error, or to adjust your decision rules if necessary, or even to replicate the experiment if it looks like something might have gone wrong with the methodology.
You can save yourself a lot of subsequent grief if you just look over some preliminary descriptive statistics based on your data set. If a mean value, the range of the observed values, or their standard deviation looks unusual, you probably should verify and validate the way the data is collected, entered and stored before too much time passes.
Excel comes equipped with a Descriptive Statistics tool in the Data Analysis add-in (which was at one time termed the Analysis ToolPak or ATP). The Descriptive Statistics tool is good news and bad news.
Figure 2.1 shows 11 values of two different variables in the range A1:B12. A univariate analysis of the variable named MPG appears in the range E3:F18, and a similar analysis of IPS appears in G3:H18. Notice that each statistic reported pertains to one variable only: None of the statistics correlates, for example, MPG with IPS, or reports the means of IPS according to specific values of MPG. The reported statistics are exclusively univariate.
If the first row in the input range has labels such as variable names, fill the Labels in First Row checkbox. This instructs Excel not to treat a label as a legitimate data value, which would usually result in an error message that the input range contains non-numeric data.
Click the Output Range option button, unless you want the results to be written to a new workbook or a new worksheet. (Worksheet Ply is just an old term for worksheet.) Careful! If you click the Output Range option button, the Input Range box is re-activated and gets filled with any cell or range that you click next. First, click the Output Range edit box and only then indicate where you want the output to begin.
Fill the Confidence Level for Mean checkbox if you want to put a confidence interval around the mean. Enter the confidence level you want in the edit box (often that will be 90, 95, or 99, taken as a percent).
The standard error of the mean is often useful when you want to test the difference between an obtained sample mean and a hypothesized value. It is also an integral part of a confidence interval placed around a sample mean.
In Excel, you use the worksheet function STDEV.P( ) when your data is the population. You use STDEV.S( ) when you have a sample. Possession of a sample occurs much more frequently than possession of a population, so the Descriptive Statistics tool returns the value that you would get if you were using the STDEV.S( ) function.
Skewness measures the symmetry in a distribution of values. An asymmetric distribution is said to be skewed. One popular way of calculating skewness is the average of the cubed standard scores (also termed z-scores):
In contrast, the Descriptive Statistics tool reports the quantity that you add to and subtract from the calculated mean so as to arrive at the confidence interval. That quantity is calculated, using Excel function syntax, as
Take a t-distribution, which is very similar to a normal curve but is a little flatter in its center and a little thicker in its tails. Its shape depends partly on the number of observations in the samples used to build the distribution. In this example, the number of observations is 11 and therefore the degrees of freedom is 10.
where v is the degrees of freedom. So in a t-distribution built on samples of size 5, the degrees of freedom is 4 and the standard deviation is the square root of (4/2), or 1.414. As the sample size increases the standard deviation decreases, so a t-distribution built on samples of size 11 has a standard deviation of 1.12.
You can use any legitimate destination name and location for the file. It will be formatted as a comma-separated values file, or csv file, so you might as well use csv as the filename extension. Also notice the use of forward slash (/) rather than backslashes (\) in the file path: R interprets a backslash as an escape character. You can instead use the forward slash (/) or two consecutive backslashes (\\).
The second AVERAGE formula includes every cell in A2:A11 except A5. Both AVERAGE( ) formulas return the same result, 46.00, and you can depend on Excel worksheet functions such as AVERAGE( ) and SUM( ) to ignore text values such as NA when they expect numeric values. 152ee80cbc
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