This document contains instructions for 8 problems involving analyzing hospital patient data, car data, survey data, and other datasets using SPSS. For problem 1, the tasks include entering hospital patient data into SPSS, recoding variables, and extracting subsets of variables. For problem 2, the tasks involve recoding variables in car data like model year and country of origin. Other problems involve merging datasets, plotting charts, conducting statistical tests, and preparing SPSS syntax.

1. M. Amir Hossain Ph.D.
Professor
ISRT, University of Dhaka
Day-4_Trouble Shooting
M. Samsul Alam
Lecturer
ISRT, University of Dhaka
Problem 1
We have a small data set containing information on some 15 hospital patients who came for their
regular check up. Systolic blood pressure (SBP), identification number (ID), age, gender, years of
schooling (Year_S), monthly income, and the place of residence of these patients are recorded.
ID SBP Age Sex Year_S Income Residence
1 130 25 M 4 3000 U
2 140 48 F 6 5000 R
3 160 50 M 8 5000 SU
4 160 47 M 12 6500 R
5 141 30 F 10 6000 R
6 144 37 M 11 6000 SU
7 155 26 F 8 6300 R
8 129 22 M 3 2500 U
9 164 51 F 5 2500 R
10 124 16 F 5 2400 SU
11 125 29 F 10 4500 U
12 136 32 M 12 8000 U
13 125 25 F 12 7500 R
14 126 28 M 14 14000 R
15 165 55 M 14 16000 SU
Here, M = Male, F = Female; U = Urban; R = Rural; SU= Semi-urban.
(a) Entry the above data in SPSS for the purpose of analysis. What can we do to make the
variables and values of the string variable more understandable to others? Perform these
operations if any.
(b) Here we see that different patients have different incomes. Instead of analyzing their
incomes numerically we are interested to classify them in some income groups according to
their incomes without changing the original variable. Create a new variable representing the
different income groups with appropriate value and variable labels.
(c) For different computational problems string variables may cause some problems that can be
avoided if these are replaced by the numeric ones. Convert the available string variables in
your data into numeric variable and also give the value and variable label.
(d) For some additional analysis we want a new data set which contains the variables ID, SBP,
SEX, and INCOME. Save this SPSS formatted data file in a suitable drive of your PC. Also
save the data file in MS Excel format keeping these same variables.

2. M. Amir Hossain Ph.D.
Professor
ISRT, University of Dhaka
Day-4_Trouble Shooting
M. Samsul Alam
Lecturer
ISRT, University of Dhaka
Problem 2
A SPSS formatted dataset ‘Car.sav’ is given. There are eight variables giving information on some
406 cars namely mpg (Miles per Gallon), engine (Engine Displacement in cubic inches), horse
(Horsepower), weight (Vehicle Weight in lbs.), accel (Time to Accelerate from 0 to 60 mph in
seconds), year (Model Year), origin (Country of Origin), and cylinder (Number of Cylinders).
Answer the followings:
(a) Instead of representing model in year we may be interested in type of model which is
defined as follows:
Type of Model Model Year
Old 70-75
Modern 76-80
Ultramodern 81-82
Now create a new variable which defines the type without altering the original year variable
according to the above instruction.
(b) Create a variable n_origin from existing variable origin where the values of origin 1, 2, 3
are replaced by 10, 20, and 30 respectivly.
(c) Compute a new variable named hiwgt whose value is ‘good’ if weight is more than 3000 lb.
(d) Find out the price (variable name should be price) of the car if the price is determined on
the basis of the number of cylinders given as follows:
(e) Additionally $500 more to be added to the cars with horsepower > 100. Now calculate the
final price of the cars creating a new variable with the consideration of this additional price.
(f) From the given dataset ‘Cars’ create the following datasets using the sub setting conditions
given below and save them all in D drive of your computer:
Name of data set Sub setting Conditions
amercar only for origin: America
japcar only for origin: Japanese
Cyl4 For cylinder = 8 and Keep variables mpg, engine,
horse, and weight
Number of cylinders in car Price
3 $ 10,000
4 $ 12,000
5 $ 14,000
6 $ 16,000
8 $ 18,000

3. M. Amir Hossain Ph.D.
Professor
ISRT, University of Dhaka
Day-4_Trouble Shooting
M. Samsul Alam
Lecturer
ISRT, University of Dhaka
Problem 3
a) Two data sets Anxiety1.sav and Anxiety2.sav contain information of six trials performed
on twelve subjects. The first data set containing the information on first four trial and the
other data set containing the remaining. Merge these two data set and save the new data as
Anxiety.sav.
b) The Anxietya.sav file containing information on few variables from 30 individuals whereas
the Anxietyb.sav file containing information on the same variables but from another 18
individuals. Append the data to obtain a single data file that will contain information of all
the 48 individuals.
Problem 4
A SPSS formatted dataset ‘Demo.sav’ is given. There are eight variables giving information on
some 6400 cases namely age (age in years), income (household income in thousands), inccat
(income category in thousands), car (price of primary vehicle), carcat (primary vehicle price
category), jobsat (job satisfaction), gender (gender) and reside (number of people in household).
Answer the followings:
(a) Draw a bar chart of mean income with carcat and mean income with jobsat. Comment on
the result.
(b) Draw histogram of age and income with normal curve. Comment on the result.
(c) Draw a pie chart of reside.
(d) Draw boxplot of income for gender. Can we use arithmetic mean to calculate the average
income for gender?
(e) Make frequency table for age, taking class interval 10 starting from age 18. Also find the
mean, median, mode, quartiles, 90th
percentile and standard deviation.
Problem 5
Glaucoma is a leading cause of blindness in the US. The following table gives the measurement of
the corneal thickness, in microns, of eight patients who had glaucoma in one eye but not in the
other.
Patient Normal Glaucoma
1
2
3
4
5
6
7
8
484
478
492
444
436
399
464
476
488
479
480
426
440
410
458
460
At the 10% level of significance, do the data provide sufficient evidence to conclude that mean
corneal thickness is greater in normal eyes than in eyes with glaucoma?

4. M. Amir Hossain Ph.D.
Professor
ISRT, University of Dhaka
Day-4_Trouble Shooting
M. Samsul Alam
Lecturer
ISRT, University of Dhaka
Problem 6
A corporation is trying to decide which of the three makes of automobile to order for its fleet-
domestic, Japanese, or European. Five cars of each type were ordered, and after 10,000 miles of
driving, the operating cost per mile of each was assessed. The accompanying results in cents per
mile were obtained.
Domestic Japanese European
18.0
17.6
15.4
19.2
16.9
20.1
15.6
16.1
15.3
15.4
19.3
17.4
15.1
18.6
16.1
(a) Set out the analysis of variance table.
(b) Test the null hypothesis that the population mean operating costs per miles are the same for
these three types of cars.
Problem 7
Using the data ‘hsb.sav’ solve the following problems.
(a) Suppose that the general population consists of 10% Hispanic, 10% Asian, 10% African
American and 70% White folks. Test whether the observed proportions from our sample
population differ significantly from these hypothesized proportions?
(b) Test whether there is any association between the type of school attended and the student’s
gender? Also test whether there is any association between the student’s gender and their
socio-economic status?

5. M. Amir Hossain Ph.D.
Professor
ISRT, University of Dhaka
Day-4_Trouble Shooting
M. Samsul Alam
Lecturer
ISRT, University of Dhaka
Problem 8
Do the subsequent tasks using SPSS based on the following sample questionnaire:
a) Construct a SPSS data entry layout for the questions listed in the given sample
questionnaire with suitable variable names and description.
b) Add appropriate value labels for the variables when required.
c) Specify the scale of measurements for all the variables.
d) Assign user defined missing values for every variable.
e) Change the settings so that the data view window will show the value labels.

6. M. Amir Hossain Ph.D.
Professor
ISRT, University of Dhaka
Day 4: Second Session
Trouble Shooting: I
M. Samsul Alam
Lecturer
ISRT, University of Dhaka