Q1. Write an R program to find the maximum and the minimum value of a given vector.

Answer:

num = c(10, 20, 30, 40, 50, 60)

print(num)  

print(paste("Maximum value of a given vector :",max(num)))

print(paste("Minimum value of a given vector :",min(num)))

 Q 2 Write a script in R to create two vectors of different lengths and give these vectors as

input to array and print addition and subtraction of those matrices

Answer :

m1 = matrix(c(1, 2, 3, 4, 5, 6), nrow = 2)

print("Matrix-1:")

print(m1)

m2 = matrix(c(0, 1, 2, 3, 0, 2), nrow = 2)

print("Matrix-2:")

print(m2)

result = m1 + m2

print("Result of addition")

print(result)

result = m1 - m2

print("Result of subtraction")

print(result)

 

Q.3 Write an R program to create a simple bar plot of five subject’s marks.

Answer:

marks = c(70, 95, 80, 74)

barplot(marks,

        main = "Comparing marks of 5 subjects",

        xlab = "Marks",

        ylab = "Subject",

        names.arg = c("English", "Science", "Math.", "Hist."),

        col = "darkred",

        horiz = FALSE)

 

Q.4 Write an R program to create a Dataframes which contain details of 5 employees and

display the details in ascending order.

Answer :

Employees = data.frame(Name=c("Saurabh S","Swati S","Kiran S", "Rajesh A","Anil Marathi"),

                      Gender=c("M","M","F","F","M"),

                      Age=c(23,22,25,26,32),

                      Designation=c("Clerk","Manager","Exective","CEO","ASSISTANT"),

                      SSN=c("123-34-2346","123-44-779","556-24-433","123-98-987","679-77-576")

                      )

print("Details of the employees:")                     

print(Employees)

 

Q. 5 Write an R program to change the first level of a factor with another level of a given

factor.

Answer:

v = c("a", "b", "a", "c", "b")

print("Original vector:")

print(v)

f = factor(v)

print("Factor of the said vector:")

print(f)

levels(f)[1] = "e"

print(f)

 

Q. 6. Write a script in R to create two vectors of different lengths and give these vectors as

input to array and print addition and subtraction of those matrices

Answer :

m1 = matrix(c(1, 2, 3, 4, 5, 6), nrow = 2)

print("Matrix-1:")

print(m1)

m2 = matrix(c(0, 1, 2, 3, 0, 2), nrow = 2)

print("Matrix-2:")

print(m2)

result = m1 + m2

print("Result of addition")

print(result)

result = m1 - m2

print("Result of subtraction")

print(result)

Regression Analysis : 

Regression Analysis includes the following steps :

Step 1 : Statement of the problem under consideration :

• The first important step in conducting any regression analysis is to specify problem and the objectives to be addressed by the regression analysis.
• The wrong formulation or the wrong understanding of the problem will give the wrong statistical inferences. The choice of variables depends upon the objectives of study and understanding of the problem.

 

Step 2 : Choice of Relevant Variables :

• Once the problem is carefully formulated and objectives have been decided, the next question is to choose the relevant variables.
• It has to keep in mind that the correct choice of variables will determine the statistical inferences correctly.
• For example, in any agricultural experiment, the yield depends on explanatory variables like quantity of fertilizer, rainfall, irrigation, temperature etc. These variables are denoted by x1, x2,….xk, as a set of k explanatory variables.

 

Step 3 : Collection of Data on Relevant Variables :

• Once the objective of study is clearly stated and the variables are chose, the next question arises is to collect data on such relevant variables. The data is essentially the measurement on these variables.
• For example, suppose we want to collect the data on age, For this, it is important to know how to record it. Then either the data of birth can be recorded which will provide the exact age on any specific date or the age in terms of completed years as on specific date.
• Moreover, it is also important to decide that whether the data has to be collected on variables as quantitative variables or qualitative variables.

 

Step 4 :  Specification of Model :

• The experimenter or the person working in the subject usually helps in determining the form of the model. Only the form of the tentative model can be ascertained and it will depend on some unknown parameters.
• For example, a general form will be like
• Y=f(x1,x2,…,xk; B1,B2,…Bk)+E
• Where E  is the random error reflecting mainly the difference in the observe values of y and the value of y obtained through the model. The form of f(x1,x2,…,xk; B1,B2,…Bk) can be linear as well as nonlinear depending on the form of parameters B1,B2,..Bk. A model is said to be linear if it is linear in parameters.

 

Step 5: Choice of Method for Fitting the Data :

• After the model has been defined and the data have been collected, the next task is to estimate the parameters of the model based on the collected data. This is also referred to as parameter estimation or model fitting.
• The most commonly used method of estimation is the lest squares method Under certain assumptions, the least square method produces estimators with desirable properties. The other estimation methods are the maximum likelihood method , ridge principal components method etc.

 

Step 6 : Fitting of Model :

• The estimation of unknown parameters using appropriate method provides the values of the parameter. Substituting these values in the equation gives us a usable model. This termed as model fitting.
• The fitted equation is used for prediction. In this case, y is termed as predicted value. Note that the fitted value is where the values used for explanatory variables correspond to one of the n observations in the data whereas predicted value is the recommended to predict the y values for the set of those values of explanatory variables which lie outside the range of data. When the values of explanatory variables are the future values of explanatory variables, the predicted values are called forecasted values.

 

Step 7  : model validation and Criticism :

• The Validity of statistical method to be used for regression analysis depends on various assumptions. These assumption are essentially the assumptions for the model and the data.
• The quality of statistical inferences heavily depends on whether these assumptions are satisfied or not. For making these assumptions to be valid and to be satisfied, care in needed from beginning of the experiment.
• One has to be care full in choosing the required assumptions and to examine whether the assumptions are valid for the given experimental conditions or not. It is also important to decide the situations in which the assumptions may not meet.
• The validation of the assumptions must be made before drawing any statistical conclusion. Any departure form validity of assumptions will be reflected in the statistical inferences. In fact, the regression analysis is an iterative process where the outputs are used to diagnose, validate, criticize and modify the inputs.

 

Step 8 : Using the chosen model for the solution of the posed problem and forecasting.

• The determination of explicit for of regression equation is the ultimate objective of regression analysis. It is finally a good and valid relationship between study variable and explanatory variables.
• The regression equation helps in understanding the interrelationships among the variables. Such regression regression equation can be used for several purposes.
• For example, to determine the role of any explanatory variable in the joint relationship in any policy formulation, to forecast the values of response variable for given set of values of explanatory variables.

Importance of Big data

• Big data helps drive efficiency, quality, and personalize products and  services producing improved levels of customer satisfaction and profit.
• In Big data, so many concepts are associated: basically there were 3 concepts volume, variety, and velocity.
• Driven by specialized analytics systems and software, big data analytics can point the way to various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals.
• Companies have searched for decades to make the best use of information to improve their business capabilities.
• Interpretation of Big Data can bring about insights which might not be immediately visible or which would be impossible to find using traditional methods.
• Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes.
• The term big data emphasizes volume or size. Size is a relative term. In the 1960s 20 Megabytes was considered large. Now data is not considered big unless it is several hundred Petabytes. Size is not the only property used to describe big data.
• Image, voice, and audio data can be analyzed for applications such as facial recognition system in security
• Structured data is data whose elements are addressable for effective analysis. It has been organized into a formatted repository that is typically a database.
• Unstructured data is a data that is which is not organized in a pre-defined manner or does not have a pre-defined data model, thus it is not a good fit for a mainstream relational database.
• Semi-structured data is information that does not reside in a relational database but that have some organizational properties that make it easier to analyze.
• It is helpful to recognize that the term analytics is not used consistently; it is used in at least three different yet related ways.
• Data analytics is concerned with extraction of actionable knowledge and insights form big data.
• Descriptive Analytics: This essentially tells what happed in the past and presents it in an easily understandable form.
• Predictive analytics :  It extrapolates form available data and tells what is expected to happen in the near future.
• Exploratory or Discovery analytics. This finds unexpected relationships among parameters in collections of big data.

 Big Data 

1. Who popularized Big Data term?

                a) John Deere                    b) John Mashed

                C) Johnny Masha              d) John Mash

Answer: b) John Mashed

 

2. Numbers, text, image, audio, and video data is

                a) Volume           b) value

                c) Verity               d) Variety

Answer : d) Variety

 

3. Real time data is

                a) fields                b) primary key

                c) unique             d) record

Answer : c) unique

 

4. ……….. is a term that is used to describe data that is high volume, high velocity, and/ or high variety.

                a) Analytics                      b) Big Data

                c) Hadoop data                 d) Big data analytics

Answer : b) Big Data

 

5. ………. Digital data is based on Relational database table.

                a) Structured                   b) Unstructured

                c) Semi-structured          d) Semi- Unstructured

Answer : a) Structured

 

6. …….. digital data is based on XML/RDF.

                a) Structured                     b) Unstructured

                c) Semi-structured           d) Semi- Unstructured

Answer : b) Unstructured

 

7. ……. Digital data is based on character and binary data.

                a) Structured                   b) Unstructured

                c) Semi-structured          d) Semi- Unstructured

Answer : c) Semi-structured

 

8. …….. is not processing source of Big Data.

                a) R                                  b) Yahoo! Pipes,

                c) Mechanical Turk          d) Data meter

Answer: d) Data meter

 

9. ……. Is concerned with extraction of actionable knowledge and insights from big data.

                a) Data analytics               b) Big Data

                c) Digital data                   d) Descriptive Analytics

Answer : a) data analytics

 

10. ……. Essentially tells what happened in the past and presents it in an easily understandable form.

                a) Data analytics               b) Big Data

                c) Digital data                   d) Descriptive Analytics

Answer : d) Descriptive Analytics

 

11. ……….. extrapolates from available data and tells what is expected to happen in the near future.

                a) Predictive Analytics    b) Big data

                c) Digital data                  d) Descriptive Analytics

Answer : a) Predictive Analytics

 

12. ………. Finds unexpected relationships among parameters is collections of big data.

                a) exploratory or discovery Analytics       b) Predictive Analytics

                c) Digital data                                           d) Descriptive Analytics

Answer :  a) exploratory or discovery Analytics

 

13. ……….. useful knowledge from data to solve business problems can be treated systematically by following a process with reasonably well-defined stages.

a) Extracting       b) Preparing

c) Prescribing     d) None

Answer : a) Extracting

 

14. ………. Answers the question “What has happed?”

                a) Descriptive analytics                  b) predictive analytics

                c) Prescriptive analytics                 d) None

Answer : a) Descriptive analytics.

 

15. ………….. answer the question “What will happen?”

                a) Descriptive analytics                  b) predictive analytics

                c) Prescriptive analytics                 d) None

Answer : b) predictive analytics

 

16. ………….. answer the question “How can we make it happen?”

                a) Descriptive analytics                  b) predictive analytics

                c) Prescriptive analytics                 d) None

Answer : c) Prescriptive analytics

 

17. ……  is most important language for Data Science.

                a) Java                  b) Ruby

                c) R                       d) None of the mentioned.

Answer : c) R

 

18. ……… phase of the data analytics lifecycle usually takes  the longest time.

                a) Phase 2 : Data preparation      b) Phase 3 : Model Planning

                c) Phase 4 : Model Building       d) Phase 5 : communicate Results

Answer : a) Phase 2 : Data preparation

 

19. When data are collected in a statistical study for only a portion or subset of all elements of interest we are using.

                a) Sample            b) Parameter

                c) Population      d) None

Answer : a) Sample

 

20. In Statistics, a population consists of

                a) All People living in a country.

                b) All people living in the city are under study.

                c) All subjects or objects whose characteristics are being studied.

                d) None of the above

Answer : c) All subjects or objects whose characteristics are being studied.

 

21. The strength (degree) of the correlation between a set of independent variables X and a dependent variable Y is measure by

                a) Coefficient of Correlation        b) Coefficient of Determination

                c) Standard error of estimate        d) All of the above.

Answer : a) Coefficient of Correlation

 

22. Correlation coefficient values lies between

                a) -1 and +1        b) 0 and 1

                c) -1 and 0           d) None of these

Answer : a) -1 and +1

 

23. In correlation, both variables are always

                a) Random          b) Non Random

                c) Same               d) None

Answer : a) Random

 

24. If two variables oppose each other then the correlation will be

                a) Positive Correlation   b) Zero Correlation

                c) Perfect Correlation     d) Negative Correlation

Answer : d) Negative Correlation.

 

25. A perfect negative correlation is signified by

                a) 0         b) 1

                c) 0.5     d) -1

Answer: d) -1

 

26. If X and Y are independent to each other, the coefficient of correlation

                a) -1       b) 0

                c) +1      d) None

Answer : a) -1

 

27. If the scatter diagram is drawn the scatter pints lie on a straight line then it indicate

                a) Regression                     b) Skewness

                c) No correlation              d) Perfect correction

Answer : c) No correlation

 

28. …………. Is the major assumption in a linear regression model.

                a) The independent variables are numeric variables.

                b) There is only one dependent variable.

                c) The relationship between the independent and dependent variables is linear.

                d) The error term is a normally distributed random variable with mean zero and constant variance.

Answer : c) The relationship between the independent and dependent variables is linear.

 

29. ……… input (independent) variables are in used a simple linear regression model.

                a) 1         b) 2

                c) 3         d) depends on the number of features/attributes involved Bottom of Form.

Answer: a) 1

 

30. …….. of the following is not a step in data analysis.

a) Obtain the data           b) Clean the data

c) EDA                            d) None of the mentioned

Answer : d) None of the mentioned

 

31. Regression analysis ………

                a) Establishes a relationship between two variables.       B) Establishes cause and effect.

                c) Measures growth.                                                         D) Measures the demand for a good.

Answer: a) Establishes a relationship between two variables.

 

32. The dependent variable is also called…..

                a) Regression                  b) Regress and

                c) Continuous variable   d) Independent

Answer: b) Regress and

 

33. The independent variable is also called…………

a) Regressor                     b) Predict and variable

c) Explained Variable      d) All of these

Answer : a) Regressor

 

34.  ………… is about developing code to enable the machine to learn to perform tasks and its basic principle is the automatic modeling of underlying processes that have generated the collected data.
                a) Data Science                  b) Machine learning (ML)

                c) Data analytics                d) All of the Mentioned

Answer : b) Machine learning (ML)

 

35. Groups of related observations are called …………….. and the procedure to organize items of a given collection into groups base on some similar features called as………………

                a) clusters, analysis                         b) regression, clustering

                c) cluster, clustering                        d) None of the Mentioned

Answer : c) cluster, clustering

 

36. ……… rule mining is a technique to identify underlying relations between different items.

                a) Association     b) analytics

                c) Learning          d) All of the Mentioned.

Answer : a) Association

 

37. ………… is a form of supervised learning. Mail service provider like Gmail, Yahoo and other use this technique to classify a new mail as spam or not spam.

                a) Machine Learning       b) Classification

                c) Regression                     d) All of the mentioned

Answer : b) Classification

 

38. Examples of supervised learning includes………….

                a) Regression                     b) Decision Tree

                c) KNN                              d) All of the Mentioned

Answer: d) All of the Mentioned

 

39. A Naive Bayes Classifier is a  ……….. machine learning algorithm which relies on the assumption of feature independent to classify input data.

a) supervised                     b) Unsupervised

c) semi-supervised            d) All of the Mentioned

Answer : a) supervised

 

40.  …………….. analysis has become one of the most widely used statistical tools for analyzing multifactor data and it is appealing because it provides a conceptually simple method for investigating functional relationships among variables.

                a) Clustering                       b) Regression

                c) Data                                d) All of the Mentioned

Answer : b) Regression