Description
In this workshop, you use the Algorithm category of the Standard Template Library.
LEARNING OUTCOMES
Upon successful completion of this workshop, you will have demonstrated the abilities to
• Copy data from a file into a sequential container
• Use the numeric library to accumulate data values
• Use a lambda expression to specify an operation on each value in a data set
• Use the algorithm library to sort data values
SUBMISSION POLICY
The in-lab section is to be completed during your assigned lab section. It is to be completed
and submitted by the end of the workshop period. If you attend the lab period and cannot
complete the in-lab portion of the workshop during that period, ask your instructor for
permission to complete the in-lab portion after the period. If you do not attend the workshop,
you can submit the in-lab section along with your at-home section (see penalties below). The
at-home portion of the lab is due on the day that is four days after your scheduled in-lab
workshop (23:59:59) (even if that day is a holiday).
All your work (all the files you create or modify) must contain your name, Seneca email and
student number.
You are responsible to back up your work regularly.
Late Submission Penalties:
• In-lab portion submitted late, with at-home portion: 0 for in-lab. Maximum of 7/10 for the
entire workshop.
• If any of in-lab, at-home or reflection portions is missing, the mark for the workshop will be
0/10.
SPECIFICATIONS – IN LAB
Introduction to Statistical Analysis
Statistical analysis uses standard measures to make general predictions about data based on a
small sample of the actual data:
• sample mean — the average of all values in the sample
• sample standard deviation — the spread of the numbers away from their mean
• sample median — the middle number in the sorted set of the values (that is, the value
separating the lower and upper halves of the data in a sorted set)
The formula for the sample mean is
𝑧𝑧𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 = 𝛴𝛴𝑖𝑖𝑧𝑧𝑖𝑖
𝑛𝑛
The symbol 𝛴𝛴 denotes sum of, 𝑖𝑖 refers to the index of the element in the sample set and 𝑛𝑛
refers to the number of elements in the sample set.
The formula for the sample standard deviation is
𝑠𝑠𝑠𝑠𝑠𝑠 = �
𝛴𝛴𝑖𝑖(𝑧𝑧𝑖𝑖 − 𝑧𝑧𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚)2
𝑛𝑛 − 1 �
1/2
A regression line provides a simple form of predicting outcomes based on sample data. The line
relates a set of independent (x) values to a corresponding set of dependent (y) values. The
number of values in each set is the same; that is, each value in the independent set has one
corresponding value in the dependent set.
The regression line is the best fit of a line to the pairs of data values. It is the line that passes
through the data points drawn on a two-dimensional (x, y) system of coordinates as closely as
possible to the data points. The line’s coefficients are:
• slope – the slope of the line in the x-y plane
• y_intercept – the y value of the line where it crosses the y-axis in the x-y plane
The formulas for these coefficients are:
𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = 𝑛𝑛(Σ𝑖𝑖𝑥𝑥𝑖𝑖𝑦𝑦𝑖𝑖) − Σ𝑖𝑖𝑥𝑥𝑖𝑖Σ𝑖𝑖𝑦𝑦𝑖𝑖
𝑛𝑛(Σ𝑖𝑖𝑥𝑥𝑖𝑖
2) − (Σ𝑖𝑖𝑥𝑥𝑖𝑖)2
𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = Σ𝑖𝑖𝑦𝑦𝑖𝑖 − 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ∗ Σ𝑖𝑖𝑥𝑥𝑖𝑖
𝑛𝑛
You can find a regression calculator here. The linear regression of a set of points is displayed
here in graphical form. The dots represent the x-y coordinates of the points in the data set and
the red line is the regression line calculated with the formulas above. You can find more
information here.
Detail Specifications
The solution to the in-lab part of this workshop consists of two modules:
• w7 (supplied)
• DataTable
Enclose your source code within the sict namespace and include the necessary header file
guard. The output from your executable running Visual Studio with the following command line
argument should look like
Command Line : C:\Users\…\Debug\in_lab.exe Simple.dat
****************************************
*** Processing file [Simple.dat]
****************************************
Data Values
————
x y
2.1000 8.0000
2.5000 12.0000
4.0000 14.0000
3.6000 10.0000
Statistics
———-
y mean = 11.0000
y sigma = 2.5820
The input for testing your solution is stored in a user-prepared file. The name of the file is
specified on the command line as shown in red above. The file is supplied with this workshop.
The contents of this file are
2.1 8
2.5 12
4.0 14
3.6 10
Each record in the file consists of a product number and its price.
DataTable Module
Design and code a class template named DataTable for holding and processing statistical data.
Your interface to the hierarchy uses the field width (int FW) defined outside the translation unit
and specifies the following public member function requirements:
• A one-argument constructor that receive a reference to an std::ifstream object, read all
records from the object and stores them in the most appropriate STL container.
• void displayData(std::ostream& os) const – a query that displays the x-y data in the
format shown above. Use the field width and precision specified in w7.
• void displayStatistics(std::ostream& os) const – a query that displays the statistics for
the current object in the format shown above. Use the field width and precision
specified in w7.
In-Lab Submission (30%)
To test and demonstrate execution of your program use the same data as shown in the output
example above.
Upload your source code to your matrix account. Compile and run your code using the latest
version of the gcc compiler and make sure that everything works properly.
Then, run the following command from your account: (replace profname.proflastname
with your professor’s Seneca userid)
~profname.proflastname/submit 345XXX_w7_lab
and follow the instructions. Replace XXX with the section letter(s) specified by your instructor.
SPECIFICATIONS – AT HOME
The at-home part of this workshop adds median and linear regression analysis to your in-lab
solution.
The output from your executable running Visual Studio with the following command line
argument should look like
Command Line : C:\Users\…\Debug\at_home.exe Simple.dat HS_College_GPA.dat
****************************************
*** Processing file [Simple.dat]
****************************************
Data Values
————
x y
2.1000 8.0000
2.5000 12.0000
4.0000 14.0000
3.6000 10.0000
Statistics
———-
y mean = 11.0000
y sigma = 2.5820
y median = 12.0000
slope = 1.9087
intercept = 5.1784
****************************************
*** Processing file [HS_College_GPA.dat]
****************************************
Data Values
————
x y
3.4500 3.7600
2.7800 2.8700
2.5200 2.5400
3.6700 3.8300
3.2400 3.2900
2.1000 2.6400
2.8200 2.8600
2.3600 2.0300
2.4200 2.8100
3.5100 3.4100
3.4800 3.6100
2.1400 2.4800
2.5900 3.2100
3.4600 3.5200
3.5100 3.4100
3.6800 3.5200
3.9100 3.8400
3.7200 3.6400
2.1500 2.1400
2.4800 2.2100
3.0900 3.1700
2.7100 3.0100
2.4600 3.1700
3.3200 3.0100
3.6100 3.7200
3.8200 3.7800
2.6400 2.5100
2.1900 2.1000
3.3400 3.2100
3.4800 3.6800
3.5600 3.4800
3.8100 3.7100
3.9200 3.8100
2.5200 2.0900
2.7100 2.1700
3.1500 2.9800
3.2200 3.2800
2.2900 2.7400
2.0300 2.1900
3.1400 3.2800
3.5200 3.6800
2.9100 3.1700
2.8300 3.1700
2.6500 3.3100
2.4100 3.0700
2.5400 2.3800
2.6600 2.9400
3.2100 2.8400
3.3400 3.1700
3.6800 3.7200
2.8400 2.1700
2.7400 2.4200
2.7100 2.4900
2.2400 3.3800
2.4800 2.0700
3.1400 3.2200
2.8300 2.7100
3.4400 3.3100
2.8900 3.2800
2.6700 3.1900
3.2400 3.2400
3.2900 3.5300
3.8700 3.7200
3.9400 3.9800
3.4200 3.0900
3.5200 3.4200
2.2400 2.0700
3.2900 3.1700
3.4100 3.5100
3.5600 3.4900
3.6100 3.5100
3.2800 3.4000
3.2100 3.3800
3.4800 3.5400
3.6200 3.4800
2.9200 3.0900
2.8100 3.1400
3.1100 3.2800
3.2800 3.4100
2.7000 3.0200
2.6200 2.9700
3.7200 4.0000
3.4200 3.3400
3.5100 3.2800
3.2800 3.3200
3.4200 3.5100
3.9000 3.6800
3.1200 3.0700
2.8300 2.7800
2.0900 3.6800
3.1700 3.3000
3.2800 3.3400
3.0200 3.1700
3.4200 3.0700
3.0600 3.1900
2.7600 2.1500
3.1900 3.1100
2.2300 2.1700
2.4800 2.1400
3.7600 3.7400
3.4900 3.2700
3.0700 3.1900
2.1900 2.9800
3.4600 3.2800
Statistics
———-
y mean = 3.1212
y sigma = 0.5066
y median = 3.2100
slope = 0.7802
intercept = 0.7279
The input for testing your solution is stored in supplied user-prepared files. The names of these
files are specified on the command line as shown in red above.
DataTable Module
Update the constructor for your DataTable template to calculate the median and regression
coefficients for the data received. Update the displayStatistics query to include output of the
median, slope and y-intercept as shown above.
Reflection
Study your final solution, reread the related parts of the course notes, and make sure that you
have understood the concepts covered by this workshop. Take your time. Explain in your own
words what you have learned in completing this workshop. Include in your explanation but do
not limit it to the following points (40%):
• The reason for using the vector container rather than any other available in the STL.
• List the STL template functions that you used in your solution.
• Identify where you used lambda expressions.
• Comment on the ease of programming associated with the STL.
To avoid deductions, refer to code in your solution as examples to support your explanations.
Include all corrections to the Quiz(zes) you have received (30%).
At-Home Submission (70%)
To test and demonstrate execution of your program use the same data as shown in the output
example above.
Upload your source code to your matrix account. Compile and run your code using the latest
version of the gcc compiler and make sure that everything works properly.
Then, run the following command from your account: (replace profname.proflastname
with your professor’s Seneca userid)
~profname.proflastname/submit 345XXX_w7_home
and follow the instructions. Replace XXX with the section letter(s) specified by your instructor.
