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Oct 30, 2024 · They do this through statistical analysis methods, which make sense of data by giving analytical insights into it. Statistical analysis drives informed approaches with business analytics. The insights gained from statistical analysis allow you to see patterns in data that have the potential to make future predictions, informing your business decision-making process.
Oct 24, 2024 · An energy company can use it to evaluate consumption trends and streamline the production schedule. 7. Survival analysis. Survival analysis focuses on time-to-event data, such as the time it takes for a machine to break down or for a customer to churn. It looks at a variable with a start time and end time.
SAS (Statistical Analysis System) is a software suite developed for advanced analytics, multivariate analysis, business intelligence, data management, and predictive analytics. Why It Rocks: SAS is a powerhouse in the corporate world, known for its stability, deep analytical capabilities, and support for large data sets.
- Overview
- What is statistical analysis?
- Main types of statistical analysis
- Other types of statistical analysis
- Statistical analysis process
- 4 common statistical analysis methods
Statistical analysis is a powerful tool businesses and organizations use to make sense of data and guide their decision-making. There are different types of statistical analysis techniques that can be applied to a wide range of data, industries and applications. Knowing the different statistical analysis methods and how to use them can help you exp...
Statistical analysis, or statistics, involves collecting, organizing and analyzing data based on established principles to identify patterns and trends. It is a broad discipline with applications in academia, business, the social sciences, genetics, population studies, engineering and several other fields. Statistical analysis has several functions...
Descriptive statistical analysis
Descriptive statistics is the simplest form of statistical analysis, using numbers to describe the qualities of a data set. It helps reduce large data sets into simple and more compact forms for easy interpretation. You can use descriptive statistics to summarize the data from a sample or represent a whole sample in a research population. Descriptive statistics uses data visualization tools such as tables, graphs and charts to make analysis and interpretation easier. However, descriptive statistics is not suitable for making conclusions. It can only represent data so you can apply more sophisticated statistical analysis tools to draw inferences.Descriptive statistics can use measures of central tendency, which uses a single value to describe a group. Mean, median and mode are used to get the central value for a given data set. For example, you can use descriptive statistical analysis to find the average age of drivers with a ticket in a municipality. Descriptive statistics can also find the measure of spread. For example, you can find the age range of drivers with a DUI and at-fault car accidents in a state. Techniques used to find a measure of spread include range, variation and standard deviation.Related: Definitive Guide To Understanding Descriptive Statistics
Inferential statistical analysis
Inferential statistical analysis is used to make inferences or draw conclusions about a larger population-based on findings from a sample group within it. It can help researchers find distinctions among groups present within a sample. Inferential statistics are also used to validate generalizations made about a population from a sample due to its ability to account for errors in conclusions made about a segment of a larger group.To perform inferential statistical analysis, researchers estimate the parameters of the population from the sample. They can also perform a test of statistical hypothesis to arrive at a confidence interval that validates or disproves the generalizations made from the sample.
Associational statistical analysis
Associational statistics is a tool researchers use to make predictions and find causation. They use it to find relationships among multiple variables. It is also used to determine whether researchers can make inferences and predictions about a data set from the characteristics of another set of data. Associational statistics is the most advanced type of statistical analysis and requires sophisticated software tools for performing high-level mathematical calculations. To measure association, researchers use a wide range of coefficients of variation, including correlation and regression analysis.
Predictive analysis
Predictive analysis uses powerful statistical algorithms and machine learning tools to predict future events and behavior based on new and historical data trends. It relies on a wide range of probabilistic techniques such as data mining, big data, predictive modeling, artificial intelligence and simulations to guess what is likely to occur in the future.Predictive analysis is a branch of business intelligence as many organizations with operations in marketing, sales, insurance and financial services rely on data to make long-term plans. It is important to note that predictive analysis can only make hypothetical forecasts and the quality of the predictions depends on the accuracy of the underlying data sets.
Prescriptive analysis
Prescriptive analysis helps organizations use data to guide their decision-making process. Companies can use tools such as graph analysis, algorithms, machine learning and simulation for this type of analysis. Prescriptive analysis helps businesses make the best choice from several alternative courses of action.Related: How Analyzing Data Can Improve Decision-Making
Exploratory data analysis
Exploratory data analysis is a technique data scientists use to identify patterns and trends in a data set. They can also use it to determine relationships among samples in a population, validate assumptions, test hypotheses and find missing data points. Companies can use exploratory data analysis to make insights based on data and validate data for errors.
1. Data collection
The first step in statistical analysis is data collection. You can collect data through primary or secondary sources such as surveys, customer relationship management software, online quizzes, financial reports and marketing automation tools. To ensure the data is viable, you can choose data from a sample that's representative of a population. For example, a company might collect data from previous customers to understand buyer behaviors.
2. Data organization
The next step after data collection is data organization. Also known as data cleaning, this stage involves identifying and removing duplicate data and inconsistencies that may prevent you from getting an accurate analysis. This step is important because it can help companies ensure their data and the conclusions they draw from the analysis are correct.
3. Data presentation
Data presentation is an extension of data cleaning, as it involves arranging the data for easy analysis. Here, you can use descriptive statistics tools to summarize the data. Data presentation can also help you determine the best way to present the data based on its arrangement.
Mean
You can calculate the mean, or average, by finding the sum of a list of numbers and then dividing the answer by the number of items in the list. It is the simplest form of statistical analysis, allowing the user to determine the central point of a data set. The formula for calculating mean is:Mean = Set of numbers / Number of items in the setExample: You can find the mean of the numbers 1, 2, 3, 4, 5 and 6 by first adding the numbers together, then dividing the answer from the first step by the number of figures in the list, which is six. The mean of the numbers is 3.5.
Standard deviation
Standard deviation (SD) is used to determine the dispersion of data points. It is a statistical analysis method that helps determine how the data spreads around the mean. A high standard deviation means the data disperses widely from the mean. A low standard deviation shows that most of the data are closer to the mean.An application of SD is to test whether participants in a survey gave similar questions. If a large percentage of respondents' answers are similar, it means you have a low standard deviation and you can apply their responses to a larger population. To calculate standard deviation, use this formula:σ2 = Σ(x − μ)2/n •σ represents standard deviation •Σ represents the sum of the data •x represents the value of the dataset •μ represents the mean of the data •n represents the number of data points in the population Example: You can calculate the standard deviation of the data set used in the mean calculation. The first step is to find the variance of the data set. To find variance, subtract each value in the data set from the mean, square the answer, add everything together and divide by the number of data points.Variance = ((3.5-1)² + (3.5-2) ² + (3.5-3) ² + (3.5-4) ² + (3.5-5) ² + (3.5-6) ²) / 6Variance = (6.25 + 2.25 + 0.25 + 0.25 + 2.25 + 6.25) / 6Variance = 17.25/6 = 2.875Next, you can calculate the square root of the variance to find the standard deviation of the data.Standard deviation = √2.875 = 1.695
Regression
Regression is a statistical technique used to find a relationship between a dependent variable and an independent variable. It helps track how changes in one variable affect changes in another or the effect of one on the other. Regression can show whether the relationship between two variables is weak, strong or varies over a time interval. The regression formula is:Y = a + b(x) •Y represents the independent variable, or the data used to predict the dependent variable •x represents the dependent variable which is the variable you want to measure •a represents the y-intercept or the value of y when x equals zero •b represents the slope of the regression graph Example: Find the dollar cost of maintaining a car driven for 40,000 miles if the cost of maintenance when there is no mileage on the car is $100. Take b as 0.02, so the cost of maintenance increases by $0.02 for every unit increase in miles driven. •Y = cost of maintaining the car •X = 40,000 miles •a = $100 •b = $0.02 Y = $100 + 0.02(40,000)Y = $900This shows that mileage affects the maintenance costs of a car.
Quantitative data analysis methods often involve statistical tests and models to quantify relationships between variables. Structured vs. Unstructured Data. Structured Data: Data that is organized in a fixed format, such as databases or spreadsheets. It is easily searchable and analyzable using traditional data analysis techniques.
- Soham Dutta
Quantitative data analysis then splits into mathematical analysis and artificial intelligence (AI) analysis. Mathematical types then branch into descriptive, diagnostic, predictive, and prescriptive. Methods falling under mathematical analysis include clustering, classification, forecasting, and optimization.
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May 10, 2023 · Often, qualitative analysis will organize the data into themes—a process which, fortunately, can be automated. Data analysts work with both quantitative and qualitative data, so it’s important to be familiar with a variety of analysis methods. Let’s take a look at some of the most useful techniques now. 3. Data analysis techniques
