BBA_Sem I_Part2.pptx many types of examples

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1. Business Statistic Unit I BBA Semester I Galgotias University

2. Learning Objectives Define statistics Become aware of a wide range of applications of statistics in business Differentiate between descriptive and inferential statistics Classify numbers by level of data and understand why doing so is important

3. STATISTICS Statistics may be defined as a science of collection, presentation, analysis and interpretation of numerical data. _ Croxton and Cowden Statistics is the science which deals with the methods of collecting, classifying, presenting, comparing and interpreting numerical data collected to throw some light on any sphere of enquiry. _ Seligman Statistical methods, broadly defined into the following two categories: Descriptive statistics: Statistical methods involving the collection, presentation, and characterization of a set of data in order to describe the various features of that set of data. In general, methods of descriptive statistics include graphic methods and numeric measures. Inferential statistics: Statistical methods which facilitate estimating the characteristic of a population or making decisions concerning a population on the basis of sample results.

4. Characteristics and of Importance Statistics Statistics are numerically expressed. It has an aggregate of facts Data are collected in systematic order It should be comparable to each other Data are collected for a planned purpose Importance: Statistics helps in gathering information about the appropriate quantitative data It depicts the complex data in graphical form, tabular form and in diagrammatic representation to understand it easily It provides the exact description and a better understanding It helps in designing the effective and proper planning of the statistical inquiry in any field It gives valid inferences with the reliability measures about the population parameters from the sample data It helps to understand the variability pattern through quantitative observations

5. Statistics in Business Economics: regional, national, and international economic performance Time-series analysis Demand analysis Forecasting techniques Finance: investments and portfolio management, analysis of profit and dividend helps to predict and decide profit and dividend analysis helps available dividends for future years. Market: analyse data on population, purchasing power, habits of the consumers, competitors, pricing, and a hoard of other aspects. Production: to improve the quality of the existing products and set quality control standards for new ones. Personnel: studies of wage rates, incentive plans, cost of living, labour turnover rates, employment trends, accident rates, performance appraisal, and training and development programmes . Statistics in Physical Sciences, Social Sciences, Medical Sciences, etc.

6. The Importance of Statistics in Business (With Examples) The field of  statistics  is concerned with collecting, analyzing , interpreting, and presenting data. In a business setting, statistics is important for the following reasons: Reason 1 : Statistics allows a business to understand consumer behavior better using des Reason 2 : Statistics allows a business to spot trends using data visualizationcriptive st Reason 3 : Statistics allows a business to understand the relationship between different variables using regression models. Reason 4 : Statistics allows a business to segment consumers into groups using cluster analysis. 

7. Reason 1: Understand Consumer Behavior Using Descriptive Statistics Descriptive statistics   are used to  describe  datasets. Businesses in almost every field use descriptive statistics to gain a better understanding of how their consumers behave. For example, a grocery store might calculate the following descriptive statistics: The mean number of customers who come in each day. The median sales order per customer. The standard deviation of the age of the customers who come in the store. The sum of the sales made each month. Using these metrics, the store can gain a strong understanding of who their customers are and how they behave

8. On the other hand, a bank might calculate the following descriptive statistics: The percentage of customers who default on their loan. The mean number of new customers who join the bank each day. The sum of the total deposits made by all customers each month. Using these metrics, the bank can get an idea of how their customers behave and how they handle their money. Not all businesses build statistical models or perform complex calculations, but just about every business uses descriptive statistics to gain a better understanding of their customers.

9. Reason 2: Spot Trends Using Data Visualization Another common way that statistics is used in business is through data visualizations such as line charts, histograms, boxplots, pie charts and other charts. These types of charts are often used to help a business spot trends. For example, a small business might create the following  combo chart  to visualize the number o f new clients and total sales they make each month:

10. Using this simple chart, the business can quickly see that both their sales and number of new clients tends to increase the most in the final quarter of the year. This can allow the business to be prepared with more staff, later hours, more inventory, etc. during this time of year. Reason 3: Understand the Relationship Between Variables Using Regression Models Another way that statistics is used in business settings is in the form of  linear regression models . These are models that allow a business to understand the relationship between one or more predictor variables and a  response variable . For example, a grocery store might track their total amount spent on print advertising, their total amount spent on online advertising, and their total revenue. They might then build the following multiple linear regression model:

11. Here’s how to interpret the  regression coefficients  in this model: For each additional dollar spent on TV advertising, the total revenue increases by  $2.55  (assuming online advertising is held constant). For each additional dollar spent on online advertising, the total revenue increases by  $4.87  (assuming TV advertising is held constant). Using this model, the grocery store can quickly see that their money is better spent on online advertising as opposed to TV advertising.

12. Reason 4: Segment Consumers into Groups Using Cluster Analysis Another way that statistics is used in business settings is in the form of  cluster analysis . This is a  machine learning technique  that allows a business to group together similar people based on different attributes Retail companies often use clustering to identify groups of households that are similar to each other. For example, a retail company may collect the following information on households: Household income Household size Head of household OccupationDistance from nearest urban area They can then feed these variables into a clustering algorithm to perhaps identify the following clusters: Cluster 1: Small family, high spenders Cluster 2: Larger family, high spenders Cluster 3: Small family, low spenders Cluster 4: Large family, low spenders The company can then send personalized advertisements or sales letters to each household based on how likely they are to respond to specific types of advertisements.

13. Concepts Process: A process is a set of conditions that repeatedly come together to transform inputs into outcomes. Population: A population (or universe) is a group of elements or observations relating to a phenomenon under study for which greater knowledge and understanding is needed. A population can be finite or infinite according to the number of observations under statistical investigation. A descriptive measure of population is called a parameter . e.g. Population mean, population variance, population standard deviation. Sample: A sample is a group of some, but not all, of the elements or observations of a population or process. A descriptive measure of a sample is called a statistic . e.g. Sample mean, sample variance, sample standard deviation.

14. Limitations of Statistics Statistics does not study qualitative Phenomena: Since statistics deals with numerical data, it cannot be applied in studying those problems which can be stated and expressed quantitatively. Statistics does not study individuals: This statement implies that a single or isolated figure cannot be considered as statistics, unless it is part of the aggregate of facts relating to any particular field of enquiry. Statistics can be misused: For proper use of statistics one should have enough skill and experience to draw accurate and sensible conclusions. Statistics as lack of complete accuracy Statistical results are true only on the avera :for example average age of a person in India is 62 years. It does not mean that every person will attain this age. On the basis of statistical methods we can say only in terms of probability and not certainty. Statistical results may be misleading

15. What is Data? Data: Data are individual pieces of factual information recorded and used for the purpose of analysis. It is the raw information from which statistics are created.   Statistics are the results of data analysis - its interpretation and presentation.  Need of Data Statistical data are the basic material needed to make an effective decision in a particular situation. The main reasons are To provide necessary inputs to a situation under study. To measure performance in an ongoing process such as production, service, etc. To enhance the quality of decision-making by enumerating alternative courses of action. To satisfy the desire to understand an unknown phenomenon. To assist in guessing the causes and probable effects of certain characteristics in given situations.

16. Types of data Categorical: Represents the qualitative aspects of individuals. Numerical: Represents the quantitative values of individuals. Discrete: which can only take certain fixed integer numerical values Continuous: which can take any numerical value. Discrete data are numerical measurements that arise from a process of counting, while continuous data are numerical measurements that arise from a process of measuring.

17. MEASUREMENT Measurement refers to the assignment of numbers in a meaningful way, and understanding measurement scales is important to interpreting the numbers assigned to people, objects, and events. Principles of measurement Numbers are ordered. The difference between numbers is ordered. The number series has a unique origin indicated by the number zero. Classification of measurement scale

18. CLASSIFICATION OF DATA Classification of data is the process of arranging data in groups/classes on the basis of certain properties. Purpose of Classification: It condenses the raw data into a form suitable for statistical analysis. It removes complexities and highlights the features of the data. It facilitates comparisons and in drawing inferences from the data. It provides information about the mutual relationships among elements of a data set. It helps in statistical analysis by separating elements of the data set into homogeneous groups.

19. Requisites of Ideal Classification The classification of data is decided after taking into consideration the nature, scope, and purpose of the investigation. It should be unambiguous: There must be only one class for each element of the data set. Classes should be exhaustive and mutually exclusive: Each element of the data set must belong to a class. Each class should be mutually exclusive so that each element must belong to only one class. It should be stable: The classification must be done in such a manner that if each time an investigation is conducted, it remains unchanged. It should be flexible: A classification should be flexible so that suitable adjustments can be made in new situations and circumstances.

20. Basis of Classification Geographical Classification: Data are classified on the basis of geographical or locational differences Chronological Classification: Data are classified on the basis of time. Qualitative Classification: Data are classified on the basis of descriptive characteristics or on the basis of attributes. Simple classification Manifold classification Quantitative Classification: Data are classified on the basis of some characteristics which can be measured, such as height, weight, income, expenditure, production, or sales. Continuous variable Discrete ( also called discontinuous ) variable

21. Collection of Data-Primary or Secondary Primary data are those which are collected for the first time and are thus original in character. Secondary data are those which have already been collected by some other persons and which have passed through the statistical machine at least once. Primary data are in the shape of raw materials to which statistical methods are applied for the purpose of analysis and interpretation. Secondary data are usually in the shape of finished products since they have been treated statistically in some form or the other. After statistical treatment the primary data lose their original shape and become secondary data

22. Collection of Data-Primary or Secondary Data which are secondary in the hands of one may be primary for others. Example: Statistics of agricultural production are secondary data for the Agriculture Department of a Government but for the purpose of calculation of national income these data are primary, because they will have to go through further analysis and their shape will not remain the same

23. Factor affecting choice of method for Data collection Nature, object and scope of the enquiry are the most important things on which the selection of the method depends. The method selected should be such that it suits the type of enquiry that is being conducted. Availability of finance: When financial resources at the disposal of the investigator are scantly, he shall have to leave aside expensive methods even though they are better than others, which are comparatively cheap. Availability of time: Some methods involve a long duration of enquiry, while with others the enquiry can be conducted in a comparatively shorter duration. The time at the disposal of the investigator thus affects the selection of the technique by which data are to be collected.

24. METHODS OF COLLECTING PRIMARY DATA Direct personal investigation. Indirect oral investigation. Local reports. Schedules and questionnaires. Nature and properties of investigator: The investigator has a keen sense of observation and is very polite and courteous. He should further acquaint himself with local conditions, customs and traditions so that he can identify himself fully with the persons from whom the information is sought.

25. Mean The  mean  of a dataset represents the average value of the dataset. It is calculated as: Mean = Σ x i  / n The mean gives us an idea of where the “center” of a dataset is located. Mean carries a piece of information from every observation in a dataset. The mean is the most common way to measure the center of a dataset, but it can actually be misleading in the following situations: When the distribution is skewed. When the distribution contains outliers. Example 3.1: In a survey of 5 cement companies, the profit (in Rs lakh) earned during a year was 15, 20, 10, 35, and 32. Find the arithmetic mean of the profit earned.

26. Calculating the Mean of a Skewed Distribution The large salaries on the right side of the distribution pull the mean away from the center of the distribution. Thus, the median does a better job of capturing the “typical” salary of a resident than the mean because the distribution is right-skewed.

27. Calculating the Mean When Outliers Are Present The large salaries on the right side of the distribution pull the mean away from the center of the distribution. Thus, the median does a better job of capturing the “typical” salary of a resident than the mean because the distribution is right-skewed.