Statistics is a section ofmathematics tangled with the variety, allotment, analysis, and explanation ofnumerical assurance. Statistics is the submission of data (Business Dictionary). There are compellingnumber true samples where statistics are worn. For example,you and a friend are at a basketball game,and he proposes you a bet that neither team will bump a home run in that game.

Should you take the bet?(Statistics/Introduction/What is Statistics)There are mainly two types of statistics, descriptive statistics andinferential statistics. Descriptive statistics accord data that portraythe information in some way. It administers a short synopsis decision ofinformation. Data can be encapsulated numerically or graphically as you want.For example, suppose a clothing shop sells pants, t-shirt and shocks. If 100cloths are sold, and 40 out of the 100 were t-shirt, then one description ofthe data on the clothssold would be that 40% were t-shirt.

(Descriptive & Inferential Statistics: Definition, Differences & Examples)In inferential statistics we benefitcapricious illustration of data which are appropriated from a population to portrayand provoke conjectures regarding the number. For example, if you want to identifythe average height of all the women in a town with a population of so manymillion residents. It will not be practical to get the height of each woman. Inthis case inferential statistics arrives into play. Inferential statistics are scarcewhen it is not appropriate or possible to review each member of acompletepopulation. (Descriptive & Inferential Statistics: Definition, Differences & Examples)A variable will be at whatever attribute,figure, or quantity that can beconsistent or counted. A variable might furthermore becalled a datacomponent.Gender, business income, birth rate, expenditure, class grades, hair color areexamples of variables.

(What are Variables?)Given below is the flow chart of types of variables: Numeric variables have amount that portrays a perceptible quantity as a number, like ‘how many’ or ‘how much’. That’s why numeric variables are quantitative variables.Categorical variables includes amount that portrays ‘quality’ or ‘distinctive’ of a data unit, like ‘what kind or ‘which class’.Therefore, categorical variables are qualitative variables and add to be characterized by a non-numeric value. A continuous variable is a numeric variable. The conclusion can obtain any value among a persuaded set of real figures. The value given to a conclusion for a continuous variable can comprise values as tiny as the apparatus of analysis allows. A continuous variable includes altitude, moment, era, and heat.

A discrete variable is a numeric variable. A conclusion can obtain a value based on a tally from a set of specific whole values. A discrete variable cannot take the value of a fraction of one value and the next closest value.

Examples of discrete variables include the number of registered cars, number of business locations, and number of children in a family, all of which measured as whole units (i.e. 1, 2, 3 cars).An ordinal variable is a categorical variable.

Conclusions can take a value that can be reasonably arranged or graded. Examples of ordinal categorical variables include academic grades (i.e. A, B, C), clothing size (i.e. small, medium, large, extra-large). A nominal variable is a categorical variable.

Conclusions can take a value that is not able to be coordinated in a cogent sequence. Examples of nominal categorical variables include sex, business type, eye color, religion and brand(What are Variables?) Frequency table and its associated terms Frequency table is a system which exhibit crude information in themanifestation which one can undoubtedly see those data held in the crudeinformation.Frequency distributions are of two types:1. Discrete frequency distribution: The transform forget ready this kind from claiming circulation is extremelystraightforward. The development of a discrete frequency circulationfrom the provided for crude information may be carried eventually byperusing the consumption of the system for count marks. In the initiallysection of the frequency table we compose every last bit could reasonably beexpected qualities of the variable starting with the least of the mostnoteworthy2. Continuous or grouped frequency distribution: whereasconstructing a grouped frequency distribution table, at first data arecollected and differentiated into groups which are called classes.

Normally, weemploy 5 to 20 classesExample of a frequency table andrelative frequency table Performance Frequency Relative Frequency Relative Frequency Percentage Early 25 25/100=0.25 25% On- time 64 64/100=0.64 64% Late 9 9/100=0.

09 9% Lost 2 2/100=0.02 2% Total 100 1 100% Chart and Graph Chart is a set of steep bars whoseareas are commensurate to the frequencies. In the histogram, variable isconsistently taken on the horizontal axis and frequencies on the vertical axis.The graphs are used to admit the attribute of discrete and continuous data. Twofrequency distributions can be compared by the shapes and patterns. (Frequency Distribution)There are two types of data and they are described asbelow:Quantitative data: Quantitative data is numerical.

Such as the number of cars,grades, number of students. This can be diagrammed. If you poll or part, youare assembling quantitative data. The quantitative data can be of two types:discrete and continuous data.Qualitativedata: Qualitativeare descriptive data which cannot be counted like, the types of a dogs, perfumesmell, design of a shirt etc Measure of Location Mean, Median and Mode are the three mainly used measure of location. 1. Mean:A mean is synonymous with the average.

This may be the best measure for symmetrical distributions. Mean isimpacted by every last bit information and most reliable.2. Median:the median is the worth in the centre of a set of data. Median does not representamazing scores. It may be not algebraically characterized.3.

Mode: the mode is the most frequent value, numberor category in a set of data. One way to remember this definition is that modesounds like most. Measure of Dispersion The measure of dispersion follows the measures of central tendency sothe common measures of dispersion are standard deviation and variance. Dispersion is the degree of variation in the data. For example, the ageof instructors {48, 49, 50, 51, 52}Range is the difference between the maximum and minimum observations.

For example, the minimum age of an instructor was 29 and maximum age was 73. Standard deviation is the square root of the variance. The variance isin square units so the standard deviation is in the same units as x Displaying andExploring data A dot plot bands the data in as little arenaas possible and classify of an individual conclusion is not lost. To evolve adot plot, each observation is simply shown as a dot along a horizontal numberline revealing the possible values of the data.Stem and leaf:One technique that is used to arrayquantitative clues in a concise form is the stem and leaf array. It is astatistical technique to present a set of data.

Each numerical value is cleftinto two parts. The dominant digit becomes the stem and the hunting digit theleaf. The stems are located along the vertical axis and the leaf values arestacked against each other along the horizontal axis.Box plot: it is a graphical array, planted onquartiles, that helps us picture a set of data. To construct a box plot, weneed five statistics;1. Theminimum value2.

Thefirst quartile (Q1)3. Themedian4. Thethird quartile (Q3) and5. Themaximum valueSkewness: Anotherattribute of a set of data is the shape. There are four shapes commonlyobserved;1. Symmetric2.

Positivelyskewed3. Negativelyskewed4. BimodalThe coefficient of skewness can range from -3 to +3. Avalue near -3, reveal negative skewness, a value such as 1.63 reveal moderatepositive skewness and a value of 0, which will occur when the mean and medianare equal, reveals the circulation is symmetrical and that there is no skewnesspresent.PEARSON’S COEFFICIENT OF SKEWNESS, sk= 3(x?- Median) /sSOFTWARECOEFFICIENT OF SKEWNESS, sk = n / (n-1) (n-2) x ? (x-x?/s)3 Describingrelationship between two variables:Whenwe review the connection between two variables we refer to the data as bivariate.One graphical approach we use to show the connection between variables iscalled scatter diagram.

To stalemate a scatter diagram we need two variables.We scale one variable along the horizontal axis of a graph and the othervariable along the vertical axisContingency Tables: Acontingency table is a cross-tabulation that concurrently compile two variablesof interest. For examples:1. Students at a university are classifiedby gender and class rank.

2. Aproduct is classified as acceptable or unacceptable and by the shift(day,afternoon, or night) on which it is manufactured.(McGraw/Hill, 2015) Probability Probability is analogous topercentage. Probability is a section of mathematics that deals with calculatingthe likelihood of a given event’s occurrence, which is assert as a numberbetween 1 and 0 (TechTarget).When the probability is 0, that is an absurd event and if the probability is 1,that is a sure event. For example, when child will born, if the probability ofgirl child is 0.6 then the probability of boy child will be 0.

4 because totalshould be 1. Probability of an event = the number of ways event A canoccur Thetotal number of possible outcomesFor example:Here, we have to find theprobability of either X and YP(X)=0.07P(Y)=0.02P(X or Y)=?As we know,The probability of either X and Yoccurring is = P(X) +P(Y) =0.07+0.

02 =0.09The probability of neither X and Yoccurring is= 1-P =1-0.09 =0.01 Thecompany that I have researched is manufacturing company of noodles. You can’tmanage what you can’t measure.

This company has been using statistics for thefollowing functions: · To forecast the production, whetherthere is a stable demand and uncertain demand.· To know the risk that is associatedwithin the operations and financial costs.· To calculate the given information toshow the statistical outcome.Statistics canhelp in increasing not only the quality of products but also the quantity thatare being manufactured.

Statistics can also help support the quality in theareas of the benefits of the business process, those mechanical and buildingprocesses. For the likelihood for similarity as considerably dependent upondate, and real time feedback, quality can be expanded almost instantly.