This document delves into the essentials of business statistics, defining its significance in data collection, analysis, and interpretation. It highlights the importance of statistics in decision-making processes within businesses, such as production planning based on consumer demand and policy formulation in banks. Key distinctions between parameters and statistics, populations and samples, descriptive and inferential statistics are also examined, alongside an exploration of variables, classifications, tabulation, and frequency distribution. This comprehensive overview serves as a foundation for understanding the vital role of statistics in business.
01 / 75Introduction to Business Statistics: Understanding Key Concepts and Applications
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Introduction to Business Statistics: Understanding Key Concepts and Applications
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What is statistics? • Numerical data relating to an aggregate of individuals, the science of collecting, analyzing and interpreting data is called statistics.
Importance of statistics in Business. • Statistics play an important role in business, because provides a quantitative basis for arriving at decisions in all matters connected with operating of business. • For example producers must know the demand of his consumers. • Statistics would help to plan production according to the demands of consumers
Conti………… • The banks make use of statistics while framing their policies. The banks have to conduct constant enquiries regularly deposits under different categories, the nature of demand for daily with-drawls etc.
Difference between Parameter and statistic. • Parameter. • A number that describes some property of a population is called parameter • For example a numerical value such as mean and mode etc…..
Conti……… • Statistic. • A number that describes some property of a sample is called statistic. • For example the average length calculated for a random(hit and miss) sample of a college students.
Difference between population and sample. • Population. • The aggregate or totality of all the individual items about which information is required is called population • For example if 1000 students in the college that we classified according to blood type.
Conti……. • Sampling. • The study of observing the single part or only a part of the population , such a part is called sampling.
Difference between Descriptive and inferential statistics. • Descriptive statistics. • Those statistics method which is concerned with collecting and describing a set of data so as to yield meaningful information. • For example teacher computes an average grade for his statistics class. The average grade describes the performance of that particular class.
Coati….. • Inferential statistics. • Those statistics methods which is concerned with the analyzing of a subset of a data leading to inference about the entire set of a data. • For example the academic records of the metric classes during the past five years at a nearby government school show that 45% of the entering freshmen eventually matriculated.
What is variable. • A quantity which may take any one of a specified set of values. • For example the height of a students, rainfall at a place, price of a commodity etc.
Discrete Variable. • A discrete variable can assume only a finite number. • For example the number of children in a family, the number of goals scored by a player etc.
Continuous variable • A continuous variable may take an infinite number of values between any two points such as the height of a student, the temperature at a place etc.
Quantitative variable. • If the values are expressed numerically the variable is said to be quantitative. • Foe example age, weight, income, number of children etc.
Qualitative variable. • If the values are not expressed in the numerical form is called qualitative variable. • For example smoking, poverty intelligence etc.
Chapter no 2 • Collection • and • Presentation • Of data.
What do you mean by classification. • Classification. • It is the process of arranging data into sequences and groups according to their common characteristics. • For example we may arrange the marks into group of 60 marks each like 01 to 59, 60 t0 119 etc.
Descriptive classification: • When the data are classified on the basis of quality which are incapable of quantitative measurement the classification is said to be descriptive. • For example classification according to the sex and marital status divide the population into six classes. • Male married • Male unmarried • Male widowed • Female married • Female unmarried • Female widowed
Numerical Classification • This type of classification is applicable to quantitative data only. • For example data related to the height, weight, income and production etc.
What do you mean by tabulation of statistical data • Tabulation simply means presenting of data through tables. • It is the next of classification in the process of statistical investigation. • To be more precise tabulation is an orderly arrangement of data into columns and rows.
Simple tabulation • A simple tabulation contains data regarding one characteristics only. • Information relating to the other characteristics being ignored.
What is frequency distribution? • A large mass of data possessing different characteristics is grouped into different classes. • The observation are determined in each class. • The arrangement of these classes into tabular form makes frequency distribution.
Class frequency and grouped data • The number of observation falling in a class makes a class frequency. • Data organized and summarized in the form of frequency distribution are called grouped data.
Number of classes and their lengths • A frequency distribution should not have too few or too many large. • Depending upon a particular data. • The number of classes should not exceed 25 and should not be less than 6.
Class Limit • The limit of the class should be so fixed that the mid point of each class interval fall on an integer and not a fraction
Class boundaries • If one have grouped frequency distribution with class limit having a gap between the upper class limit of one class and the lower class limit of the next class
Class frequency • The frequency of a class interval is the total number of items falling in that class interval • Also called class tally sheet • Usually after every four lines in a class the fifth item is marked by horizontal or slanted lines across the strokes
Q : make a group frequency distribution from the following data . • 106 107 76 82 109 107 115 93 95 123 125 • 111 92 86 70 126 68 130 129 139 119 115 • 128 100 186 84 99 113 204 111 141 136 123 90 115 • 98 110 78 185 162 178 140 112 173 146 158 194 • 148 90 107 181 131 75 184 104 110 80 118 82. • By scanning the data we find that the largest weight is 204 and the lowest is 68 so the range is 204-68=136 • Decide on the number of classes into which the data are to be grouped we used H.A Sturges rule. • K= 1+3.3logN. • Where k denotes the number of classes and N is the total number of observation.
Q: arrange the data given below in an array and construct a frequency distribution using a class interval of 5 indicate the class boundaries and class limit clearly • 79.4, 71.6, 95.5, 73, 74.2, 81.8, 90.6, 55.9, 75.2, 81.9, 68.9, 74.2, 80.7, 65.7, 67.6, 82.9, 88.1, 77.8, 69.4, 83.2, 82.7, 73.8, 64.2, 63.9, 58.3, 48.6, 83.5, 70.8, 72.1, 71.9, 59.4, 77.6 • Ans: the value of variate range from 48.6 to 95.5. we take class interval of length 5. • No. of classes = range • class interval • 95.5 – 48.6 = 9.38 = 10 • 5
What is simple bar chart: • The simple bar chart is particularly appropriate for a linear or one dimension comparison. The scale for construction of simple bar chart should be such as facilitates the representation of largest bar quite conveniently.
Multiple bar chart • In this type of chart we represent two or more than two sets of a data in one chart than more than one chart is used. • This can be explained with the help of example, the following table give the imports and exports of Pakistan for year 1992 to1997.
