Problem

You have developed an application for the task of registering crop information and have now been given crop data for analysis. The crop data includes the (x, y) coordinates of a robotic weed scanner (or unmanned ground vehicle (UGV) ) as it moves through a crop. Your task is to develop a console based representation of the movement patterns of the robotic scanner. Along side coordinates, the robot has also reported weed classification results for each (x, y) coordinate. The weed identification algorithms is using a lot of resources and for this reason graphics will be avoided in place of a console representation.

The robot is programmed to move on a grid and has an algorithm that converts GPS coordinates into a grid based system. In this scenario the robot will start moving at location (0, 0) and can move 1 space at a time in the x and y dimensions. That is, it can only increment x and/or y by 1 at each step. You will not need to implement this part, a text based data file will be provided to you. Location (0, 0) will be set to the top left hand corner and the grid will be set to a fixed width and height of 10 x 10 units. The text file will contain coordinates on each line with a 3 integer classification result (x, y, R1, R2, R3).  The result will require processing through a single artificial neuron based classifier, the neuron will contain 3 weights to match the 3 integers of the result.

This calculation through the neuron can be expressed:

Class = Round(R₁ X W₁ + R₂ X W₂ + R₃ X W₃)

The calculation should produce a result between 0 and 1, rounding will then produce 1 when the number is greater than 0.5 and 0 if it is below, for this you can use pythons inbuilt round function. Class 1 in this case will be a weed and class 0 will be no weed detected. The round function is being used in place of a step based activation function, typically depicted as g(.).

To complete this task, you will need to develop three functions that process and display the results in a console based grid. The first function will take a file name and return a 10x10 list of lists containing and representing the values from each line of the text file (x, y, R₁, R₂, R₃). This function will be called read_file (described below) and will take 1 parameter (the file name). The result values (R₁, R₂, R₃) will need to be stored as a list as well, this creates what is essentially a list of lists of lists. While this does sound complex, it can be created by appending the list results at each 2 dimensional index of a 2 dimensional list, which can be initialised as an empty list for each potential location in a 10x10 grid. The index will be the x and y values, that is, the first 2 values from each line of the text file.

Example, for a single set of values (1, 1, 4, 9, 1):

coord_results[x][y] = [R1, R2, R3]

or

coord_results[1][1] = [4,9,1]

Where coord_results is a 10x10 list of lists (or 2 dimensional list).

The second function will be called classify and will take the 3 result values and multiply them by their associated weight value. Using 3 iterations of a loop the values can be added together and rounded to produce a classification result. This function will take two parameters, a 3 float weight list and a 3 integer result. After the calculation the rounded result can be returned as an integer.

The third function will be called display_results and this function will display the text based grid in the console. A text data file and the expected output is depicted below. The function will print 1 line of the grid at a time in a loop and will require a nested loop to loop through all possible locations in the grid (all x and all y). Inside the nested loops you will need to check a set of conditions. If the index (x,y) has an empty list (meaning the robotic scanner did not pass over it) two spaces will be drawn. If the classify function returns 1 on the result for the coordinate an ' x' will be drawn, otherwise a full stop will be drawn (' .').

The three weight values that you will need to store in your application are provided below, these weights have been determined using a machine learning method to produce accurate classification.

An example data file is provided below with results displayed.

Note that program specifications are not always clear. If you are uncertain about any aspect, you are typically better off asking than making assumptions. Please use the appropriate discussion forum to ask for clarification, if required.

Example Interactions

Given the following list of weights:

0.03
0.04
0.03

Given the following text file (attached below - crop1.dat):

0 0 5 4 7
1 1 4 9 1
1 2 3 2 7
1 3 6 4 6
2 4 8 2 1
3 5 3 7 2 
4 6 5 7 1 
4 7 6 2 1
5 8 7 2 7
6 9 9 8 6
7 9 1 1 1
8 9 6 2 4
9 9 1 2 8
9 8 6 8 6
8 7 4 5 0
7 6 5 8 6
7 5 8 2 1
7 4 3 2 5
8 3 8 1 3
9 2 1 7 1

Your display output should display the following output.

 x                   
   x . x             
         .           
           .         
             . .     
                 .   
                   x 
         . . x     . 
       .       .   . 
     .           x . 

The time series showing the sales of a particular product over the past 12 months is contained in the Excel Online file below. Construct a spreadsheet to answer the following questions.

1. Use a=0.2 to compute the exponential smoothing forecasts for the time series (to 2 decimals).

   Month	Time-Series Value	Forecast
   1	105
   2	130
   3	125
   4	100
   5	90
   6	120
   7	150
   8	135
   9	95
   10	75
   11	100
   12	105
   13

2. Use a smoothing constant of a=0.5 to compute the exponential smoothing forecasts (to 2 decimals).

   Month	Time-Series Value	Forecast
   1	105
   2	130
   3	125
   4	100
   5	90
   6	120
   7	150
   8	135
   9	95
   10	75
   11	100
   12	105
   13

   Compute MSE (to 2 decimals).

   MSE ( a= 0.2 ) : (___)
   MSE ( a = 0.5) : (___)

1 : An array a is defined to be self-referential if for i=0 to a.length-1, a[i] is the count of the number of times that the value i appears in the array. As the following table indicates, {1, 2, 1, 0} is a self-referential array.

Here are some examples of arrays that are not self-referential:

{2, 0, 0} is not a self-referential array. There are two 0s and no 1s. But unfortunately there is a 2 which contradicts a[2] = 0.

{0} is not a self-referential array because there is one 0, but since a[0] = 0, there has to be no 0s.

{1} is not a self-referential array because there is not a 0 in the array as required by a[0] = 1.

Self-referential arrays are rare. Here are the self-referential arrays for arrays of lengths up to 10 elements:

{1, 2, 1, 0} (see above)
{2, 0, 2, 0} (there are two 0s, no 1s, two 2s and no 3s
{2, 1, 2, 0, 0}  (there are two 0s, one 1, two 2s, no 3s and no 4s)
{3, 2, 1, 1, 0, 0, 0} (there are three 0s, two 1s, one 2, one 3, no 4s, 5s or 6s)
{4, 2, 1, 0, 1, 0, 0, 0} (there are four 0s, two 1s, one 2, no 3s, one 4, and no 5s, 6s, or 7s)
{5, 2, 1, 0, 0, 1, 0, 0, 0} (there are five 0s, two 1s, one 2, no 3s or 4s, one 5, and no 6s, 7s, or 8s)
{6, 2, 1, 0, 0, 0, 1, 0, 0, 0} (there are six 0s, two 1s, one 2, no 3s, 4s, or 5s, one 6, and no 7s, 8s, or 9s)

Write a function named isSelfReferential that returns 1 if its array argument is self-referential, otherwise it returns 0.

If you are programming in Java or C#, the function signature is
   int isSelfReferential(int[ ] a)

If you are programming in C or C++, the function signature is
   int isSelfReferential(int a[ ], int len) where len is the number of elements in the array

An automobile repair shop is concerned about customer satisfaction in terms of the entire experience customers receive from the repair shop. In order to quantify the customer experience, three critical service characteristics have been identified:

Complaints of a failure to fix the vehicle.

Delay beyond the promised pickup time.

Complaints of damage to the inside/outside of the vehicle during repair.

A level of zero defects is the long-term goal. In order to address this goal, statistics have been collected over the past few months regarding these critical characteristics (see Table).

(A)Based on the Pareto principal, what would be the strategy for decreasing the total number of defects? (

B) With respect to the number of delays from promised completion time, does the process appear in-control? Assuming that special causes can be eliminated, what is the best estimate of the process capability?

(C) How is the process performing with respect to the number of items not fixed properly? What is the process capability?

(A)Number of Vehicles in sample

(B) Number of items not fixed properly

(C) Number of delays from promised completion times

(D)Number of damaged items in repair

A marketing organization wishes to study the effects of four sales methods on weekly sales of a product. The organization employs a randomized block design in which three salesman use each sales method. The results obtained are given in the following table, along with the Excel output of a randomized block ANOVA of these data.

A marketing organization wishes to study the effects of four sales methods on weekly sales of a product. The organization employs a randomized block design in which three salesman use each sales method. The results obtained are given in the following table, along with the Excel output of a randomized block ANOVA of these data.

(a) Test the null hypothesis H₀ that no differences exist between the effects of the sales methods (treatments) on mean weekly sales. Set α = .05. Can we conclude that the different sales methods have different effects on mean weekly sales?

F = 22.32, p-value = .0012; (Click to select)RejectDo not reject H₀: there is (Click to select)no differencea difference in effects of the sales methods (treatments) on mean weekly sales.

(b) Test the null hypothesis H₀ that no differences exist between the effects of the salesmen (blocks) on mean weekly sales. Set α = .05. Can we conclude that the different salesmen have different effects on mean weekly sales?

F = 73.08, p-value = .0001; (Click to select)Do not rejectReject H₀: salesman (Click to select)do notdo have an effect on sales

(c) Use Tukey simultaneous 95 percent confidence intervals to make pairwise comparisons of the sales method effects on mean weekly sales. Which sales method(s) maximize mean weekly sales? (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)

Many regions in North and South Carolina and Georgia have experienced rapid population growth over the last 10 years. It is expected that the growth will continue over the next 10 years. This has motivated many of the large grocery store chains to build new stores in the region. The Kelley’s Super Grocery Stores Inc. chain is no exception. The director of planning for Kelley’s Super Grocery Stores wants to study adding more stores in this region. He believes there are two main factors that indicate the amount families spend on groceries. The first is their income and the other is the number of people in the family. The director gathered the following sample information.

Develop a correlation matrix. (Round your answers to 3 decimal places. Negative amounts should be indicated by a minus sign.)

Determine the regression equation. (Round your answer to 3 decimal places.)

How much does an additional family member add to the amount spent on food? (Round your answer to the nearest dollar amount.)

What is the value of R²? (Round your answer to 3 decimal places.)

Complete the ANOVA (Leave no cells blank - be certain to enter "0" wherever required. Round SS, MS to 4 decimal places and F to 2 decimal places.)

State the decision rule for 0.05 significance level. H₀: = β₁ = β₂ = 0; H₁: Not all β_i's = 0. (Round your answer to 2 decimal places.)

Complete the table given below. (Leave no cells blank - be certain to enter "0" wherever required. Round Coefficient, SE Coefficient, P to 4 decimal places and T to 2 decimal places.)

Predictor SE Coefficient t p-value

Constant

Income

Size

A marketing organization wishes to study the effects of four sales methods on weekly sales of a product. The organization employs a randomized block design in which three salesman use each sales method. The results obtained are given in the following table, along with the Excel output of a randomized block ANOVA of these data. Salesman, j Sales Method, i A B C 1 38 30 25 2 43 28 26 3 31 24 20 4 32 19 17 ANOVA: Two-Factor without Replication SUMMARY Count Sum Average Variance Method 1 3 93 31.0000 43.0000 Method 2 3 97 32.3333 86.3333 Method 3 3 75 25.0000 31.0000 Method 4 3 68 22.6667 66.3333 Salesman A 4 144 36.00 31.3333 Salesman B 4 101 25.25 23.5833 Salesman C 4 88 22.00 18.0000 ANOVA Source of Variation SS df MS F P-Value F crit Rows 194.9167 3 64.9722 16.36 .0027 4.7571 Columns 429.5000 2 214.7500 54.06 .0001 5.1433 Error 23.8333 6 3.97222 Total 648.2500 11 (a) Test the null hypothesis H0 that no differences exist between the effects of the sales methods (treatments) on mean weekly sales. Set α = .05. Can we conclude that the different sales methods have different effects on mean weekly sales? F = 16.36, p-value = .0027; H0: there is in effects of the sales methods (treatments) on mean weekly sales. (b) Test the null hypothesis H0 that no differences exist between the effects of the salesmen (blocks) on mean weekly sales. Set α = .05. Can we conclude that the different salesmen have different effects on mean weekly sales? F = 54.06, p-value = .0001; H0: salesman have an effect on sales (c) Use Tukey simultaneous 95 percent confidence intervals to make pairwise comparisons of the sales method effects on mean weekly sales. Which sales method(s) maximize mean weekly sales? (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.) Method 1 – Method 2: [ , ] Method 1 – Method 3: [ , ] Method 1 – Method 4: [ , ] Method 2 – Method 3: [ , ] Method 2 – Method 4: [ , ] Method 3 – Method 4: [ , ]

A marketing organization wishes to study the effects of four sales methods on weekly sales of a product. The organization employs a randomized block design in which three salesman use each sales method. The results obtained are given in the following table, along with the Excel output of a randomized block ANOVA of these data.

(a) Test the null hypothesis H₀ that no differences exist between the effects of the sales methods (treatments) on mean weekly sales. Set α = .05. Can we conclude that the different sales methods have different effects on mean weekly sales?

F = 9.25, p-value = .0114; (Click to select)RejectDo not reject H₀: there is (Click to select)no differencea difference in effects of the sales methods (treatments) on mean weekly sales.

(b) Test the null hypothesis H₀ that no differences exist between the effects of the salesmen (blocks) on mean weekly sales. Set α = .05. Can we conclude that the different salesmen have different effects on mean weekly sales?

F = 33.36, p-value = .0006; (Click to select)Do not rejectReject H₀: salesman (Click to select)do notdo have an effect on sales

(c) Use Tukey simultaneous 95 percent confidence intervals to make pairwise comparisons of the sales method effects on mean weekly sales. Which sales method(s) maximize mean weekly sales? (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)

A marketing organization wishes to study the effects of four sales methods on weekly sales of a product. The organization employs a randomized block design in which three salesman use each sales method. The results obtained are given in the following table, along with the Excel output of a randomized block ANOVA of these data.

(a) Test the null hypothesis H₀ that no differences exist between the effects of the sales methods (treatments) on mean weekly sales. Set α = .05. Can we conclude that the different sales methods have different effects on mean weekly sales?

F = 16.14, p-value = .0028; (Do not reject OR Reject) H₀: there is (Difference OR No difference )a in effects of the sales methods (treatments) on mean weekly sales.

(b) Test the null hypothesis H₀ that no differences exist between the effects of the salesmen (blocks) on mean weekly sales. Set α = .05. Can we conclude that the different salesmen have different effects on mean weekly sales?

F = 36.67, p-value = .0004; ((Do not reject OR Reject) t H₀: salesman (Do not OR Do)do notdo have an effect on sales

(c) Use Tukey simultaneous 95 percent confidence intervals to make pairwise comparisons of the sales method effects on mean weekly sales. Which sales method(s) maximize mean weekly sales? (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)