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# DIPLOMA IN STATISTICS

Rs.6,000.00 Rs.3,000.00

statistics course is generally intended for individuals majoring in the natural sciences, mathematics, or secondary math education.

Students manipulate data and estimate the probability of an event occurring.

## Description

Course Name: DIPLOMA IN STATISTICS
Course Id: DIS/Q1001.
Education Qualification: 12th Pass.

Duration: 370 Hrs.

How You will Get Diploma Certificate:

Step 1- Select your Course for Certification.

Step 2- Click on Enroll Now.

Step 3- Proceed to Enroll Now.

Step 4- Fill Your Billing Details and Proceed to Pay.

Step 5- You Will be Redirected to Payment Gateway, Pay Course and Exam Fee by Following Options.

Card(Debit/Credit), Wallet, Paytm, Net banking, UPI and Google pay.

Step 6- After Payment You will receive Study Material on your email id.

Step 7- After Completion of  Course Study give Online Examination.

Step 8- After Online Examination you will get Diploma Certificate soft copy(Scan Copy) and Hard Copy(Original With Seal and Sign).

Step 9- After Certification you will receive Prospect Job Opportunities as per your Interest Area.

Online Examination Detail:

Duration- 60 minutes in each Module.
No. of Questions- 30. (Multiple Choice Questions) in each Module.
Maximum Marks- 600, Passing Marks- 40%.
There is No Negative marking in this module.

Benefits of Certification:

• Government Authorized Assessment Agency Certification.
• Free Job Assistance as per your Interest Area.

#### Syllabus

##### Diploma in Statistics
 Name of Paper M. Marks Pass Marks General nature and scope of statistical methods 100 40 Elementary ideas about statistical populations 100 40 Elementary idea of probability 100 40 Design and analysis of experiments 100 40 Elementary theory of index numbers 100 40 Statistical methods in Psychology and Education 100 40

##### General nature and scope of statistical methods

Data Collection and Tabulation: Meanings, Definitions and Applications of Statistics, Measurements and Scale, Measurements of qualitative data, Methods of data collection, Types of data. Meanings, Definitions and Applications of Statistics, Measurements and Scale, Measurements of qualitative data, Methods of data collection, Types of data, Representation of Data- I (Graphical representation): Graphical representation of frequency distribution, Histogram, Frequency polygon, Frequency curve, Ogive. Measures of Central Tendency and Dispersion: Types of measures of central tendency, Arithmetic mean, Fundamental Theorems on Arithmetic mean, Geometric mean, Harmonic mean, Median, Mode, Percentiles, Deciles, and Quartiles, Measures of Dispersion : Types of measures of Dispersion, Range, Mean Deviation, Variance and Standard deviation, Effect of change of origin and scale, Relationship between measures of central tendency and measures of dispersion, Coefficient of variation. Moments, Skewness and Kurtosis: Definition of moments, raw moments for ungrouped data, raw moments for grouped data, Central moments, Factorial moments, Interrelationship between various moments, effect of change of origin and scale on moments, Charlier’s checks, Sheppard”s correction for moments.

##### Elementary ideas about statistical populations

Estimation of population mean, confidence intervals for the parameters of a normal distribution (one sample and two sample problems), The basic idea of significance test, Null and alternative hypothesis. Type I & Type II errors, level of significance. Tests of hypotheses for the parameters of a normal distribution (one sample and two sample problems), Demographic Methods: Introduction, measurement of population, rates and ratios of vital events. Measurement of mortality: CDR, SDR (w.r.t. Age and sex), IMR, Standardized death rates. Life(mortality)tables: definition of its main functions and uses. Measurement of fertility and reproduction: CBR, GFR, and TFR. Measurement of population growth: GRR, NRR, Testing and confidence intervals of equality of two population variances, Concept of population and sample, complete enumeration versus sampling, sampling and nonsampling errors. Types of sampling: non-probability and probability sampling, basic principle of sample survey, simple random sampling with and without replacement.

##### Elementary idea of probability

Random experiments and Probability: Deterministic and random experiments, Sample space, Events, Algebra of Events, Axiomatic definition of Probability, Classical definition of Probability, Statistical definition of probability, Addition Theorem of Probability, Conditional Probability: Conditional probability, Multiplicative theorem of Probability, Independent events, Partition of sample space, Baye’s Theorem. Random Variables and Probability Distributions: Definition and types of random variable, Cumulative distribution function and its properties, Probability Mass Function, Probability Density Function, Definition and types of Mathematical Expectation, Moments in terms of expectation, Mathematical and Multiplication theorems of Expectation, other theorems on expectation. Univariate Distributions: Bernoulli distribution, Binomial Distribution, mean and variance of binomial distribution, Moments, Moments Generating Function, Additive and Multiplicative property.

##### Design and analysis of experiments

Analysis of variance (ANOVA) for one way and two way classified data (one observation per cell) Experimental designs: Role, historical perspective, terminology, experimental error, basic principles, uniformity trials, fertility contour maps, choice of size and shape of plots and blocks.        Basic designs: Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD) – layout, model and statistical analysis, relative efficiency, analysis with missing observations. Factorial experiments: advantages and disadvantages, notations and concepts, 22, 23 … 2n and 32 factorial experiments, design and its analysis and applications. Total and Partial confounding for 2n (n≤5), 32 and 33. Factorial experiments in a single replicate. Advantages and disadvantages. Balanced Incomplete Block Design (BIBD) – parameters, relationships among its parameters. Analysis of Variance, Linear Models and Analysis of Variance, Design of Experiment, Basic Principles of Design of Experiments, Completely Randomized Design, Randomized Block Design, and Efficiency of RBD, Missing Plot Technique, Latin Square Design, and Efficiency of LSD.

##### Elementary theory of index numbers

Index Number: General Theory: Definition & Construction of an Index number, Price Relatives, Quantity or Volume Relatives, Value Relatives, Link & Chain Relatives, Problem involved in computation of an Index Number, Index Numbers: Important Formulae: Introduction, Calculation of Index Number, Laspeyre’s, Paasche’s, Marshall Edgeworth’s, fisher’s formulae, other indices, Quantity Index, Criteria of good Index Number, Introduction, Construction & Computation of Consumer Price Index Number (CPI), Steps in construction of CPI, Use & Limitations of CPI, Base Shifting of Index Numbers, Splicing of Index Number Series, Deflating the Index Number, Index of Industrial Production. Index numbers: Definition, Criteria for a good index number, different types of index numbers, Construction of index numbers of prices and quantities, consumer price index number, Uses and limitations of index numbers.

##### Statistical methods in Psychology and Education

To help students develop knowledge and understanding of the application of Statistics within Psychology, To help students develop critical thinking for application of appropriate statistical analysis in Psychological research, nature of psychological variables and how to measure them using appropriate scale. methods of drawing inferences and conclusions for hypothesis testing by using appropriate statistical analysis, knowledge of the general principles of psychological research and the commonest elementary designs. Knowledge of more sophisticated research strategies and designs, understand the role of descriptive and inferential statistics as part of quantitative research methodology. Demonstrate the usefulness of descriptive and inferential statistics as part of quantitative research methodology. Describe quantitative results using descriptive statistics.