Descriptive statistics can be useful for two purposes: 1) to provide basic information about variables in a dataset and 2) to highlight potential relationships between variables. The three most common descriptive statistics can be displayed graphically or pictorially and are measures of: Graphical/Pictorial Methods.

Descriptive statistics are used to describe or summarize the characteristics of a sample or data set, such as a variable’s mean, standard deviation, or frequency.

Interpret the key results for Descriptive Statistics

1. Step 1: Describe the size of your sample.
2. Step 2: Describe the center of your data.
3. Step 3: Describe the spread of your data.
4. Step 4: Assess the shape and spread of your data distribution.
5. Compare data from different groups.

The objective of descriptive statistics is to summarize data. To provide a method to convey impressions about the data.

Descriptive statistics is a branch of statistics that aims at describing a number of features of data usually involved in a study. The main purpose of descriptive statistics is to provide a brief summary of the samples and the measures done on a particular study.

They help us understand and describe the aspects of a specific set of data by providing brief observations and summaries about the sample, which can help identify patterns.

Some of the most common and convenient statistical tools to quantify such comparisons are the F-test, the t-tests, and regression analysis. Because the F-test and the t-tests are the most basic tests they will be discussed first.

Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).

The most commonly used statistical technique is multiple regression analysis (and its variations such as regression in stages or two-stage least squares regression analysis), although other multivariate techniques are also widely used (such as factorial analysis or canonical analysis) [KLE 07].

Excel

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There are two main methods of Data Analysis:

• Qualitative Analysis. This approach mainly answers questions such as ‘why,’ ‘what’ or ‘how.
• Quantitative Analysis. Generally, this analysis is measured in terms of numbers.
• Text analysis.
• Statistical analysis.
• Diagnostic analysis.
• Predictive analysis.
• Prescriptive Analysis.

Software Access

Statistical analysis software, such as SPSS, is often used to analyze quantitative data. Qualitative data describes qualities or characteristics. It is collected using questionnaires, interviews, or observation, and frequently appears in narrative form.

Top Qualitative Data Analysis Software NVivo, ATLAS. ti, Provalis Research Text Analytics Software, Quirkos, MAXQDA, Dedoose, Raven’s Eye, Qiqqa, webQDA, HyperRESEARCH, Transana, F4analyse, Annotations, Datagrav are some of the top Qualitative Data Analysis Software.

Qualitative Analysis For example, field notes, interviews, video, audio, open-ended survey questions all provide qualitative data for analysis.