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For qualitative variables, appropriate descriptive statistics include frequencies and proportions, as they help summarize categorical data and show the distribution of different categories. For quantitative variables, measures such as mean, median, mode, range, variance, and standard deviation are suitable because they provide insights into the central tendency, spread, and overall distribution of numerical data. The choice of statistics depends on the nature of the data: qualitative data is categorical and non-numeric, while quantitative data is numeric and can be measured.
There are to classes of methods to find the minimum of a function: analytical and numerical. Analytical methods are precise but cannot be applied always. For example, we can find the minimum of a function by setting its first derivative to zero and solve for the variable and then check the second derivative (must be positive). Numerical methods involve the application of steps repeatedly until an acceptable estimate of the solution is found. Numerical methods include Newton method, steepest descent method, golden section method, Simplex method, to name just a few.
A graph is a visual representation of numerical or other information, often used for comparative purposes. Mathematical graphs include those in geometry that indicate points, lines, and curves within a Cartesian coordinate system. Other types of graphs (bar graphs, pie graphs) display numerical values or percentages as lengths or areas, and may use colors to indicate the data for more than one set of values.
Statistical Data: Statistical data science involves the collection, interpretation, and validation of data. It involves a variety of statistical operations performed with some statistical tools without having prior statistical knowledge. There are several software packages for performing statistical data analysis, including SAS (Statistical Analysis System), SPSS (Statistical Package for Social Sciences), etc. There are Different Types of Statistical data : Numerical Data: The data can serve as a measure, such as a person's height, weight, IQ, or blood pressure, or they can serve as a count, such as the number of shares a person owns, a dog's number of teeth, or the number of pages in a book you finish before falling asleep. 2.Categorical data : Categorical data are attributes that can take on numerical values (such as the numbers "1" and "2" indicating male and female, respectively). Ordinary Data : In ordinal data, categorical data are mixed with numerical data. The data fall into categories, but the numbers placed on the categories have meaning. For example, rating a restaurant on a scale from 0 (lowest) to 4 (highest) stars provides ordinal data. Statistical Methods: It is a statistical method that extracts information from research data and provides ways to assess the robustness of research outputs with mathematical formulas, models, and techniques. There are different types of Statistical Methods: Descriptive Methods : A descriptive method involves every step in the analysis and interpretation process, such as the collection of data, the tabulation of data, the measurement of central tendency, the measurement of dispersion, as well as the analysis of time series. This method is also otherwise called descriptive statistics . 2 Analytical methods : This method comprises all those methods which help analyze and compare any two or more variables. These include correlation analysis, regression analysis, attribute association analysis, and the like. This method is also referred to as analytic analysis. Inductive Methods :A generalization procedure consists of all procedures that lead to an estimation of a phenomenon using random observations or partial data, such as interpolation and extrapolation. This methods is also otherwise called inductive statistics. Inferential Methods :In other words, it is a method of drawing conclusions about the characteristics of a population based on samples of data. This method includes theories such as sampling theory, different tests of significance, statistical control, etc. This method is also otherwise called inferential statistics. Applied Methods :These methods are used to solve real-life problems, such as statistical quality control, sampling surveys, linear programming, inventory control, and other procedures. This article will help you to learn more and understand better . If you want to Know more in deep , you can consult with professional experts like Silver lake Consulting , Spss - tutor who help you to solve each and every possible problem.
A sample is a subset of a population that is selected for research or analysis. It represents a smaller group that is studied to make inferences about the larger population. A sampling frame, on the other hand, is a list of all the elements in the population from which the sample is drawn. It serves as the source from which the sample is selected and should ideally include all members of the population.
Types of statistical data include; 1.Numerical 2.Categorical 3.Ordinal
A numerical characteristic of a population is known as a parameter, which summarizes a specific aspect of the population's attributes. Common examples include the population mean (average), population variance, or population proportion. These parameters provide valuable insights into the overall behavior and distribution of the population being studied. For example, the mean income of a city's residents is a numerical characteristic that reflects the economic status of the population.
Categorical data is a type of data that represents categories or groups. It is qualitative data that includes labels or names that have no specific order or numerical value. Examples include gender, color, and type of fruit.
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No, a crosstabulation does not have to include both categorical and quantitative variables. It is primarily used to summarize the relationship between two categorical variables. However, quantitative variables can be categorized into groups or bins to create a crosstabulation, but it's not a requirement.
Categorical variables take on a limited and at times a fixed number of value possibilities. If in fields such as Compute Science or Mathematics, they are referred to as enumerated types. In some cases possible values of a variable may be classified as levels.
Quantitative data is measurable and numerical in nature. In contrast, qualitative data is any data that is not numerical and cannot be measured, only observed. Examples of quantitative data include age, height, year, and population. Examples of qualitative data include color, gender, country, and city.
Tabular form in a lab report refers to presenting data in a structured table format. This can help organize numerical or categorical data, making it easier to interpret and analyze results. Tables typically include headings, rows, and columns to clearly display experimental data.
Graphs that represent situations without numerical values are often referred to as qualitative graphs. These graphs illustrate relationships and trends using non-numeric data, such as categories or descriptions. They can depict concepts like trends over time or comparisons between different groups, emphasizing the nature of the relationships rather than precise measurements. Examples include bar graphs for categorical data or line graphs showing general trends.
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Probability is a numerical value and there must bea number, not just include one.
No the population does not include animals. It only includes the human population.