314.MODERN ELECTRONIC GADGETS DATA CREATION AND ANALYSIS USING PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC SGPLOT | PROC RANK | MACROS WITH INTCK DERIVATIONS

MODERN ELECTRONIC GADGETS DATA CREATION AND ANALYSIS USING PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC SGPLOT | PROC RANK | MACROS WITH INTCK DERIVATIONS

options nodate nonumber nocenter;

1) Create gadgets dataset using DATA Step

data work.gadgets;

infile datalines dsd dlm='|';

length Model $40 Brand $20 Category $15 Release_Date $10;

input Model :$40. Brand :$20. Category :$15. RAM_GB Storage_GB Battery_mAh

      Price_USD Rating Release_Date :$10.;

datalines;

iPhone 14 Pro|Apple|Phone|6|128|3200|999|4.6|2022-09-16

Galaxy S23|Samsung|Phone|8|256|3900|899|4.5|2023-02-17

Pixel 7|Google|Phone|8|128|4355|599|4.3|2022-10-13

MacBook Air M2|Apple|Laptop|8|256|5200|1199|4.7|2022-07-15

Dell XPS 13|Dell|Laptop|16|512|6000|999|4.4|2023-01-20

ThinkPad X1 Carbon|Lenovo|Laptop|16|1024|5800|1299|4.5|2022-09-05

Apple Watch Series 9|Apple|Smartwatch|1|32|300|399|4.6|2023-09-12

Galaxy Watch5|Samsung|Smartwatch|1|16|410|279|4.2|2022-08-26

AirPods Pro 2|Apple|Earbuds|1|1|  |249|4.4|2022-09-23

Surface Pro 9|Microsoft|Tablet|8|256|5000|999|4.1|2023-10-03

OnePlus 11|OnePlus|Phone|12|256|5000|699|4.3|2023-02-07

Pixel Watch 2|Google|Smartwatch|1|32|306|299|4.0|2023-05-10

Oppo Find X6|Oppo|Phone|12|256|4800|749|4.1|2023-03-15

Huawei MateBook 14|Huawei|Laptop|16|512|5600|899|4.0|2022-11-25

Bose QC Earbuds|Bose|Earbuds|1|1|  |279|4.2|2023-04-01

;

run;

proc print data=work.gadgets;

run;

OUTPUT:

Obs	Model	Brand	Category	Release_Date	RAM_GB	Storage_GB	Battery_mAh	Price_USD	Rating
1	iPhone 14 Pro	Apple	Phone	2022-09-16	6	128	3200	999	4.6
2	Galaxy S23	Samsung	Phone	2023-02-17	8	256	3900	899	4.5
3	Pixel 7	Google	Phone	2022-10-13	8	128	4355	599	4.3
4	MacBook Air M2	Apple	Laptop	2022-07-15	8	256	5200	1199	4.7
5	Dell XPS 13	Dell	Laptop	2023-01-20	16	512	6000	999	4.4
6	ThinkPad X1 Carbon	Lenovo	Laptop	2022-09-05	16	1024	5800	1299	4.5
7	Apple Watch Series 9	Apple	Smartwatch	2023-09-12	1	32	300	399	4.6
8	Galaxy Watch5	Samsung	Smartwatch	2022-08-26	1	16	410	279	4.2
9	AirPods Pro 2	Apple	Earbuds	2022-09-23	1	1	.	249	4.4
10	Surface Pro 9	Microsoft	Tablet	2023-10-03	8	256	5000	999	4.1
11	OnePlus 11	OnePlus	Phone	2023-02-07	12	256	5000	699	4.3
12	Pixel Watch 2	Google	Smartwatch	2023-05-10	1	32	306	299	4.0
13	Oppo Find X6	Oppo	Phone	2023-03-15	12	256	4800	749	4.1
14	Huawei MateBook 14	Huawei	Laptop	2022-11-25	16	512	5600	899	4.0
15	Bose QC Earbuds	Bose	Earbuds	2023-04-01	1	1	.	279	4.2

2) Clean and enhance dataset 

(convert Release_Date to SAS date, compute Battery_Life_est when missing, and other derived vars) 

data work.gadgets;

  set work.gadgets;

  /* Convert Release_Date from ISO text to SAS date */

  ReleaseDate = input(Release_Date, yymmdd10.);

  format ReleaseDate yymmdd10.;

  drop Release_Date;

  /* Standardize missing numeric battery for earbuds: use typical battery-life estimates */

  if Category = 'Earbuds' and (Battery_mAh = .) then do;

    /* earbuds tend to have small internal batteries — estimate playback minutes */

    Battery_mAh = 60; 

  end;

  /* Derive Battery_Life_hours: a heuristic estimate (not measured hours) */

  /* Use different heuristics by category */

  length Battery_Life_hours 8;

  if Category in: ('Phone') then Battery_Life_hours = round((Battery_mAh/400)*6, 0.1); 

  else if Category = 'Laptop' then Battery_Life_hours = round((Battery_mAh/600)*8, 0.1);

  else if Category = 'Smartwatch' then Battery_Life_hours = round((Battery_mAh/100)*1.2, 0.1);

  else if Category = 'Earbuds' then Battery_Life_hours = round((Battery_mAh/60)*6, 0.1);

  else Battery_Life_hours = .;

  /* Device age in months from ReleaseDate to today */

  Device_Age_Months = intck('month', ReleaseDate, today());

  /* Price per GB of RAM (simple affordability metric) */

  if RAM_GB > 0 then Price_per_RAM = round(Price_USD / RAM_GB, 0.01);

  else Price_per_RAM = .;

  /* Create numeric Rating_Scaled 0-100 for easier scoring */

  Rating_Scaled = round(Rating * 20, 1);

  format Battery_Life_hours 6.1 Price_per_RAM 6.2 Rating_Scaled 6.1;

run;

proc print data=work.gadgets;

run;

OUTPUT:

Obs	Model	Brand	Category	RAM_GB	Storage_GB	Battery_mAh	Price_USD	Rating	ReleaseDate	Battery_Life_hours	Device_Age_Months	Price_per_RAM	Rating_Scaled
1	iPhone 14 Pro	Apple	Phone	6	128	3200	999	4.6	2022-09-16	48.0	38	166.50	92.0
2	Galaxy S23	Samsung	Phone	8	256	3900	899	4.5	2023-02-17	58.5	33	112.38	90.0
3	Pixel 7	Google	Phone	8	128	4355	599	4.3	2022-10-13	65.3	37	74.88	86.0
4	MacBook Air M2	Apple	Laptop	8	256	5200	1199	4.7	2022-07-15	69.3	40	149.88	94.0
5	Dell XPS 13	Dell	Laptop	16	512	6000	999	4.4	2023-01-20	80.0	34	62.44	88.0
6	ThinkPad X1 Carbon	Lenovo	Laptop	16	1024	5800	1299	4.5	2022-09-05	77.3	38	81.19	90.0
7	Apple Watch Series 9	Apple	Smartwatch	1	32	300	399	4.6	2023-09-12	3.6	26	399.00	92.0
8	Galaxy Watch5	Samsung	Smartwatch	1	16	410	279	4.2	2022-08-26	4.9	39	279.00	84.0
9	AirPods Pro 2	Apple	Earbuds	1	1	60	249	4.4	2022-09-23	6.0	38	249.00	88.0
10	Surface Pro 9	Microsoft	Tablet	8	256	5000	999	4.1	2023-10-03	.	25	124.88	82.0
11	OnePlus 11	OnePlus	Phone	12	256	5000	699	4.3	2023-02-07	75.0	33	58.25	86.0
12	Pixel Watch 2	Google	Smartwatch	1	32	306	299	4.0	2023-05-10	3.7	30	299.00	80.0
13	Oppo Find X6	Oppo	Phone	12	256	4800	749	4.1	2023-03-15	72.0	32	62.42	82.0
14	Huawei MateBook 14	Huawei	Laptop	16	512	5600	899	4.0	2022-11-25	74.7	36	56.19	80.0
15	Bose QC Earbuds	Bose	Earbuds	1	1	60	279	4.2	2023-04-01	6.0	31	279.00	84.0

3) A macro to compute a composite Performance Score with configurable weights 

%macro perf_score(in=work.gadgets, out=work.gadgets_scored, w_ram=0.25,

                  w_batt=0.30, w_storage=0.15, w_price=0.15, w_rating=0.15);

  data &out.;

    set &in.;

    /* Normalize components to 0-100 before weighting */

    /* We'll use winsorized bounds to reduce effect of outliers */

  /* For simplicity compute theoretical maxs (could be computed dynamically with PROC SQL) */

    max_ram = 16; /* typical upper bound used here */

    max_batt = 6000;

    max_storage = 1024;

    min_price = 199; /* lower bound for normalization */

    max_price = 1299;

    /* Normalize each metric 0-100 */

    ram_norm = min(100, max(0, (RAM_GB / max_ram) * 100));

    batt_norm = min(100, max(0, (Battery_mAh / max_batt) * 100));

    stor_norm = min(100, max(0, (Storage_GB / max_storage) * 100));

    /* For price, lower price is better — invert */

    if Price_USD >= max_price then price_norm = 0;

    else if Price_USD <= min_price then price_norm = 100;

    else price_norm = 100 * (max_price - Price_USD) / (max_price - min_price);

    rating_norm = min(100, max(0, Rating_Scaled));

    /* Composite score */

    Performance_Score = round(ram_norm*&w_ram. + batt_norm*&w_batt. + stor_norm*&w_storage. + price_norm*&w_price. + rating_norm*&w_rating., 0.1);

    label ram_norm = 'RAM (0-100)'

          batt_norm = 'Battery (0-100)'

          stor_norm = 'Storage (0-100)'

          price_norm = 'Price (inverted, 0-100)'

          rating_norm = 'Rating (0-100)'

          Performance_Score = 'Composite Performance Score';

  run;

  proc print;

  run;

%mend perf_score;

4) Run the scoring macro multiple times with different weight sets to show repeated performance scoring 

%perf_score(in=work.gadgets, out=work.gadgets_score_default);

OUTPUT:

Obs	Model	Brand	Category	RAM_GB	Storage_GB	Battery_mAh	Price_USD	Rating	ReleaseDate	Battery_Life_hours	Device_Age_Months	Price_per_RAM	Rating_Scaled	max_ram	max_batt	max_storage	min_price	max_price	ram_norm	batt_norm	stor_norm	price_norm	rating_norm	Performance_Score
1	iPhone 14 Pro	Apple	Phone	6	128	3200	999	4.6	2022-09-16	48.0	38	166.50	92.0	16	6000	1024	199	1299	37.50	53.333	12.500	27.2727	92	45.1
2	Galaxy S23	Samsung	Phone	8	256	3900	899	4.5	2023-02-17	58.5	33	112.38	90.0	16	6000	1024	199	1299	50.00	65.000	25.000	36.3636	90	54.7
3	Pixel 7	Google	Phone	8	128	4355	599	4.3	2022-10-13	65.3	37	74.88	86.0	16	6000	1024	199	1299	50.00	72.583	12.500	63.6364	86	58.6
4	MacBook Air M2	Apple	Laptop	8	256	5200	1199	4.7	2022-07-15	69.3	40	149.88	94.0	16	6000	1024	199	1299	50.00	86.667	25.000	9.0909	94	57.7
5	Dell XPS 13	Dell	Laptop	16	512	6000	999	4.4	2023-01-20	80.0	34	62.44	88.0	16	6000	1024	199	1299	100.00	100.000	50.000	27.2727	88	79.8
6	ThinkPad X1 Carbon	Lenovo	Laptop	16	1024	5800	1299	4.5	2022-09-05	77.3	38	81.19	90.0	16	6000	1024	199	1299	100.00	96.667	100.000	0.0000	90	82.5
7	Apple Watch Series 9	Apple	Smartwatch	1	32	300	399	4.6	2023-09-12	3.6	26	399.00	92.0	16	6000	1024	199	1299	6.25	5.000	3.125	81.8182	92	29.6
8	Galaxy Watch5	Samsung	Smartwatch	1	16	410	279	4.2	2022-08-26	4.9	39	279.00	84.0	16	6000	1024	199	1299	6.25	6.833	1.563	92.7273	84	30.4
9	AirPods Pro 2	Apple	Earbuds	1	1	60	249	4.4	2022-09-23	6.0	38	249.00	88.0	16	6000	1024	199	1299	6.25	1.000	0.098	95.4545	88	29.4
10	Surface Pro 9	Microsoft	Tablet	8	256	5000	999	4.1	2023-10-03	.	25	124.88	82.0	16	6000	1024	199	1299	50.00	83.333	25.000	27.2727	82	57.6
11	OnePlus 11	OnePlus	Phone	12	256	5000	699	4.3	2023-02-07	75.0	33	58.25	86.0	16	6000	1024	199	1299	75.00	83.333	25.000	54.5455	86	68.6
12	Pixel Watch 2	Google	Smartwatch	1	32	306	299	4.0	2023-05-10	3.7	30	299.00	80.0	16	6000	1024	199	1299	6.25	5.100	3.125	90.9091	80	29.2
13	Oppo Find X6	Oppo	Phone	12	256	4800	749	4.1	2023-03-15	72.0	32	62.42	82.0	16	6000	1024	199	1299	75.00	80.000	25.000	50.0000	82	66.3
14	Huawei MateBook 14	Huawei	Laptop	16	512	5600	899	4.0	2022-11-25	74.7	36	56.19	80.0	16	6000	1024	199	1299	100.00	93.333	50.000	36.3636	80	78.0
15	Bose QC Earbuds	Bose	Earbuds	1	1	60	279	4.2	2023-04-01	6.0	31	279.00	84.0	16	6000	1024	199	1299	6.25	1.000	0.098	92.7273	84	28.4

%perf_score(in=work.gadgets, out=work.gadgets_score_batt_focused, w_ram=0.15, w_batt=0.50, w_storage=0.10, w_price=0.10, w_rating=0.15);

OUTPUT:

Obs	Model	Brand	Category	RAM_GB	Storage_GB	Battery_mAh	Price_USD	Rating	ReleaseDate	Battery_Life_hours	Device_Age_Months	Price_per_RAM	Rating_Scaled	max_ram	max_batt	max_storage	min_price	max_price	ram_norm	batt_norm	stor_norm	price_norm	rating_norm	Performance_Score
1	iPhone 14 Pro	Apple	Phone	6	128	3200	999	4.6	2022-09-16	48.0	38	166.50	92.0	16	6000	1024	199	1299	37.50	53.333	12.500	27.2727	92	50.1
2	Galaxy S23	Samsung	Phone	8	256	3900	899	4.5	2023-02-17	58.5	33	112.38	90.0	16	6000	1024	199	1299	50.00	65.000	25.000	36.3636	90	59.6
3	Pixel 7	Google	Phone	8	128	4355	599	4.3	2022-10-13	65.3	37	74.88	86.0	16	6000	1024	199	1299	50.00	72.583	12.500	63.6364	86	64.3
4	MacBook Air M2	Apple	Laptop	8	256	5200	1199	4.7	2022-07-15	69.3	40	149.88	94.0	16	6000	1024	199	1299	50.00	86.667	25.000	9.0909	94	68.3
5	Dell XPS 13	Dell	Laptop	16	512	6000	999	4.4	2023-01-20	80.0	34	62.44	88.0	16	6000	1024	199	1299	100.00	100.000	50.000	27.2727	88	85.9
6	ThinkPad X1 Carbon	Lenovo	Laptop	16	1024	5800	1299	4.5	2022-09-05	77.3	38	81.19	90.0	16	6000	1024	199	1299	100.00	96.667	100.000	0.0000	90	86.8
7	Apple Watch Series 9	Apple	Smartwatch	1	32	300	399	4.6	2023-09-12	3.6	26	399.00	92.0	16	6000	1024	199	1299	6.25	5.000	3.125	81.8182	92	25.7
8	Galaxy Watch5	Samsung	Smartwatch	1	16	410	279	4.2	2022-08-26	4.9	39	279.00	84.0	16	6000	1024	199	1299	6.25	6.833	1.563	92.7273	84	26.4
9	AirPods Pro 2	Apple	Earbuds	1	1	60	249	4.4	2022-09-23	6.0	38	249.00	88.0	16	6000	1024	199	1299	6.25	1.000	0.098	95.4545	88	24.2
10	Surface Pro 9	Microsoft	Tablet	8	256	5000	999	4.1	2023-10-03	.	25	124.88	82.0	16	6000	1024	199	1299	50.00	83.333	25.000	27.2727	82	66.7
11	OnePlus 11	OnePlus	Phone	12	256	5000	699	4.3	2023-02-07	75.0	33	58.25	86.0	16	6000	1024	199	1299	75.00	83.333	25.000	54.5455	86	73.8
12	Pixel Watch 2	Google	Smartwatch	1	32	306	299	4.0	2023-05-10	3.7	30	299.00	80.0	16	6000	1024	199	1299	6.25	5.100	3.125	90.9091	80	24.9
13	Oppo Find X6	Oppo	Phone	12	256	4800	749	4.1	2023-03-15	72.0	32	62.42	82.0	16	6000	1024	199	1299	75.00	80.000	25.000	50.0000	82	71.1
14	Huawei MateBook 14	Huawei	Laptop	16	512	5600	899	4.0	2022-11-25	74.7	36	56.19	80.0	16	6000	1024	199	1299	100.00	93.333	50.000	36.3636	80	82.3
15	Bose QC Earbuds	Bose	Earbuds	1	1	60	279	4.2	2023-04-01	6.0	31	279.00	84.0	16	6000	1024	199	1299	6.25	1.000	0.098	92.7273	84	23.3

%perf_score(in=work.gadgets, out=work.gadgets_score_budget_focused, w_ram=0.10, w_batt=0.20, w_storage=0.10, w_price=0.40, w_rating=0.20);

OUTPUT:

Obs	Model	Brand	Category	RAM_GB	Storage_GB	Battery_mAh	Price_USD	Rating	ReleaseDate	Battery_Life_hours	Device_Age_Months	Price_per_RAM	Rating_Scaled	max_ram	max_batt	max_storage	min_price	max_price	ram_norm	batt_norm	stor_norm	price_norm	rating_norm	Performance_Score
1	iPhone 14 Pro	Apple	Phone	6	128	3200	999	4.6	2022-09-16	48.0	38	166.50	92.0	16	6000	1024	199	1299	37.50	53.333	12.500	27.2727	92	45.0
2	Galaxy S23	Samsung	Phone	8	256	3900	899	4.5	2023-02-17	58.5	33	112.38	90.0	16	6000	1024	199	1299	50.00	65.000	25.000	36.3636	90	53.0
3	Pixel 7	Google	Phone	8	128	4355	599	4.3	2022-10-13	65.3	37	74.88	86.0	16	6000	1024	199	1299	50.00	72.583	12.500	63.6364	86	63.4
4	MacBook Air M2	Apple	Laptop	8	256	5200	1199	4.7	2022-07-15	69.3	40	149.88	94.0	16	6000	1024	199	1299	50.00	86.667	25.000	9.0909	94	47.3
5	Dell XPS 13	Dell	Laptop	16	512	6000	999	4.4	2023-01-20	80.0	34	62.44	88.0	16	6000	1024	199	1299	100.00	100.000	50.000	27.2727	88	63.5
6	ThinkPad X1 Carbon	Lenovo	Laptop	16	1024	5800	1299	4.5	2022-09-05	77.3	38	81.19	90.0	16	6000	1024	199	1299	100.00	96.667	100.000	0.0000	90	57.3
7	Apple Watch Series 9	Apple	Smartwatch	1	32	300	399	4.6	2023-09-12	3.6	26	399.00	92.0	16	6000	1024	199	1299	6.25	5.000	3.125	81.8182	92	53.1
8	Galaxy Watch5	Samsung	Smartwatch	1	16	410	279	4.2	2022-08-26	4.9	39	279.00	84.0	16	6000	1024	199	1299	6.25	6.833	1.563	92.7273	84	56.0
9	AirPods Pro 2	Apple	Earbuds	1	1	60	249	4.4	2022-09-23	6.0	38	249.00	88.0	16	6000	1024	199	1299	6.25	1.000	0.098	95.4545	88	56.6
10	Surface Pro 9	Microsoft	Tablet	8	256	5000	999	4.1	2023-10-03	.	25	124.88	82.0	16	6000	1024	199	1299	50.00	83.333	25.000	27.2727	82	51.5
11	OnePlus 11	OnePlus	Phone	12	256	5000	699	4.3	2023-02-07	75.0	33	58.25	86.0	16	6000	1024	199	1299	75.00	83.333	25.000	54.5455	86	65.7
12	Pixel Watch 2	Google	Smartwatch	1	32	306	299	4.0	2023-05-10	3.7	30	299.00	80.0	16	6000	1024	199	1299	6.25	5.100	3.125	90.9091	80	54.3
13	Oppo Find X6	Oppo	Phone	12	256	4800	749	4.1	2023-03-15	72.0	32	62.42	82.0	16	6000	1024	199	1299	75.00	80.000	25.000	50.0000	82	62.4
14	Huawei MateBook 14	Huawei	Laptop	16	512	5600	899	4.0	2022-11-25	74.7	36	56.19	80.0	16	6000	1024	199	1299	100.00	93.333	50.000	36.3636	80	64.2
15	Bose QC Earbuds	Bose	Earbuds	1	1	60	279	4.2	2023-04-01	6.0	31	279.00	84.0	16	6000	1024	199	1299	6.25	1.000	0.098	92.7273	84	54.7

5) Use PROC RANK to create rank order by Performance_Score

proc rank data=work.gadgets_score_default out=work.gadgets_ranked_default ties=low descending;

  var Performance_Score;

  ranks Perf_Rank_Default;

run;

proc print data=work.gadgets_ranked_default;

run;

OUTPUT:

Obs	Model	Brand	Category	RAM_GB	Storage_GB	Battery_mAh	Price_USD	Rating	ReleaseDate	Battery_Life_hours	Device_Age_Months	Price_per_RAM	Rating_Scaled	max_ram	max_batt	max_storage	min_price	max_price	ram_norm	batt_norm	stor_norm	price_norm	rating_norm	Performance_Score	Perf_Rank_Default
1	iPhone 14 Pro	Apple	Phone	6	128	3200	999	4.6	2022-09-16	48.0	38	166.50	92.0	16	6000	1024	199	1299	37.50	53.333	12.500	27.2727	92	45.1	10
2	Galaxy S23	Samsung	Phone	8	256	3900	899	4.5	2023-02-17	58.5	33	112.38	90.0	16	6000	1024	199	1299	50.00	65.000	25.000	36.3636	90	54.7	9
3	Pixel 7	Google	Phone	8	128	4355	599	4.3	2022-10-13	65.3	37	74.88	86.0	16	6000	1024	199	1299	50.00	72.583	12.500	63.6364	86	58.6	6
4	MacBook Air M2	Apple	Laptop	8	256	5200	1199	4.7	2022-07-15	69.3	40	149.88	94.0	16	6000	1024	199	1299	50.00	86.667	25.000	9.0909	94	57.7	7
5	Dell XPS 13	Dell	Laptop	16	512	6000	999	4.4	2023-01-20	80.0	34	62.44	88.0	16	6000	1024	199	1299	100.00	100.000	50.000	27.2727	88	79.8	2
6	ThinkPad X1 Carbon	Lenovo	Laptop	16	1024	5800	1299	4.5	2022-09-05	77.3	38	81.19	90.0	16	6000	1024	199	1299	100.00	96.667	100.000	0.0000	90	82.5	1
7	Apple Watch Series 9	Apple	Smartwatch	1	32	300	399	4.6	2023-09-12	3.6	26	399.00	92.0	16	6000	1024	199	1299	6.25	5.000	3.125	81.8182	92	29.6	12
8	Galaxy Watch5	Samsung	Smartwatch	1	16	410	279	4.2	2022-08-26	4.9	39	279.00	84.0	16	6000	1024	199	1299	6.25	6.833	1.563	92.7273	84	30.4	11
9	AirPods Pro 2	Apple	Earbuds	1	1	60	249	4.4	2022-09-23	6.0	38	249.00	88.0	16	6000	1024	199	1299	6.25	1.000	0.098	95.4545	88	29.4	13
10	Surface Pro 9	Microsoft	Tablet	8	256	5000	999	4.1	2023-10-03	.	25	124.88	82.0	16	6000	1024	199	1299	50.00	83.333	25.000	27.2727	82	57.6	8
11	OnePlus 11	OnePlus	Phone	12	256	5000	699	4.3	2023-02-07	75.0	33	58.25	86.0	16	6000	1024	199	1299	75.00	83.333	25.000	54.5455	86	68.6	4
12	Pixel Watch 2	Google	Smartwatch	1	32	306	299	4.0	2023-05-10	3.7	30	299.00	80.0	16	6000	1024	199	1299	6.25	5.100	3.125	90.9091	80	29.2	14
13	Oppo Find X6	Oppo	Phone	12	256	4800	749	4.1	2023-03-15	72.0	32	62.42	82.0	16	6000	1024	199	1299	75.00	80.000	25.000	50.0000	82	66.3	5
14	Huawei MateBook 14	Huawei	Laptop	16	512	5600	899	4.0	2022-11-25	74.7	36	56.19	80.0	16	6000	1024	199	1299	100.00	93.333	50.000	36.3636	80	78.0	3
15	Bose QC Earbuds	Bose	Earbuds	1	1	60	279	4.2	2023-04-01	6.0	31	279.00	84.0	16	6000	1024	199	1299	6.25	1.000	0.098	92.7273	84	28.4	15

proc rank data=work.gadgets_score_batt_focused out=work.gadgets_ranked_batt ties=low descending;

  var Performance_Score;

  ranks Perf_Rank_Batt;

run;

proc print data=work.gadgets_ranked_batt;

run;

OUTPUT:

Obs	Model	Brand	Category	RAM_GB	Storage_GB	Battery_mAh	Price_USD	Rating	ReleaseDate	Battery_Life_hours	Device_Age_Months	Price_per_RAM	Rating_Scaled	max_ram	max_batt	max_storage	min_price	max_price	ram_norm	batt_norm	stor_norm	price_norm	rating_norm	Performance_Score	Perf_Rank_Batt
1	iPhone 14 Pro	Apple	Phone	6	128	3200	999	4.6	2022-09-16	48.0	38	166.50	92.0	16	6000	1024	199	1299	37.50	53.333	12.500	27.2727	92	50.1	10
2	Galaxy S23	Samsung	Phone	8	256	3900	899	4.5	2023-02-17	58.5	33	112.38	90.0	16	6000	1024	199	1299	50.00	65.000	25.000	36.3636	90	59.6	9
3	Pixel 7	Google	Phone	8	128	4355	599	4.3	2022-10-13	65.3	37	74.88	86.0	16	6000	1024	199	1299	50.00	72.583	12.500	63.6364	86	64.3	8
4	MacBook Air M2	Apple	Laptop	8	256	5200	1199	4.7	2022-07-15	69.3	40	149.88	94.0	16	6000	1024	199	1299	50.00	86.667	25.000	9.0909	94	68.3	6
5	Dell XPS 13	Dell	Laptop	16	512	6000	999	4.4	2023-01-20	80.0	34	62.44	88.0	16	6000	1024	199	1299	100.00	100.000	50.000	27.2727	88	85.9	2
6	ThinkPad X1 Carbon	Lenovo	Laptop	16	1024	5800	1299	4.5	2022-09-05	77.3	38	81.19	90.0	16	6000	1024	199	1299	100.00	96.667	100.000	0.0000	90	86.8	1
7	Apple Watch Series 9	Apple	Smartwatch	1	32	300	399	4.6	2023-09-12	3.6	26	399.00	92.0	16	6000	1024	199	1299	6.25	5.000	3.125	81.8182	92	25.7	12
8	Galaxy Watch5	Samsung	Smartwatch	1	16	410	279	4.2	2022-08-26	4.9	39	279.00	84.0	16	6000	1024	199	1299	6.25	6.833	1.563	92.7273	84	26.4	11
9	AirPods Pro 2	Apple	Earbuds	1	1	60	249	4.4	2022-09-23	6.0	38	249.00	88.0	16	6000	1024	199	1299	6.25	1.000	0.098	95.4545	88	24.2	14
10	Surface Pro 9	Microsoft	Tablet	8	256	5000	999	4.1	2023-10-03	.	25	124.88	82.0	16	6000	1024	199	1299	50.00	83.333	25.000	27.2727	82	66.7	7
11	OnePlus 11	OnePlus	Phone	12	256	5000	699	4.3	2023-02-07	75.0	33	58.25	86.0	16	6000	1024	199	1299	75.00	83.333	25.000	54.5455	86	73.8	4
12	Pixel Watch 2	Google	Smartwatch	1	32	306	299	4.0	2023-05-10	3.7	30	299.00	80.0	16	6000	1024	199	1299	6.25	5.100	3.125	90.9091	80	24.9	13
13	Oppo Find X6	Oppo	Phone	12	256	4800	749	4.1	2023-03-15	72.0	32	62.42	82.0	16	6000	1024	199	1299	75.00	80.000	25.000	50.0000	82	71.1	5
14	Huawei MateBook 14	Huawei	Laptop	16	512	5600	899	4.0	2022-11-25	74.7	36	56.19	80.0	16	6000	1024	199	1299	100.00	93.333	50.000	36.3636	80	82.3	3
15	Bose QC Earbuds	Bose	Earbuds	1	1	60	279	4.2	2023-04-01	6.0	31	279.00	84.0	16	6000	1024	199	1299	6.25	1.000	0.098	92.7273	84	23.3	15

6) Summary statistics with PROC MEANS and PROC UNIVARIATE 

proc means data=work.gadgets_score_default mean median std min max n;

  var RAM_GB Storage_GB Battery_mAh Battery_Life_hours Price_USD Rating_Scaled Performance_Score;

  title 'Numeric Summary: GADGETS (Default Performance Score)';

run;

OUTPUT:

Numeric Summary: GADGETS (Default Performance Score)

The MEANS Procedure

Variable	Label	Mean	Median	Std Dev	Minimum	Maximum	N
RAM_GB Storage_GB Battery_mAh Battery_Life_hours Price_USD Rating_Scaled Performance_Score	Composite Performance Score	7.6666667 244.4000000 3332.73 46.0214286 723.0000000 86.5333333 53.0600000	8.0000000 256.0000000 4355.00 61.9000000 749.0000000 86.0000000 57.6000000	5.8022984 272.5078767 2379.20 32.8496149 355.4835741 4.5018515 19.9292965	1.0000000 1.0000000 60.0000000 3.6000000 249.0000000 80.0000000 28.4000000	16.0000000 1024.00 6000.00 80.0000000 1299.00 94.0000000 82.5000000	15 15 15 14 15 15 15

proc univariate data=work.gadgets_score_default;

  var Performance_Score Price_USD Battery_Life_hours;

  histogram Performance_Score / normal;

  inset mean std median / position=ne;

  title 'Univariate Analysis for Key Metrics';

run;

title;

OUTPUT:

Univariate Analysis for Key Metrics

The UNIVARIATE Procedure

Variable: Performance_Score (Composite Performance Score)

Moments
N	15	Sum Weights	15
Mean	53.06	Sum Observations	795.9
Std Deviation	19.9292965	Variance	397.176857
Skewness	-0.0029729	Kurtosis	-1.4578113
Uncorrected SS	47790.93	Corrected SS	5560.476
Coeff Variation	37.5599255	Std Error Mean	5.14572222

Basic Statistical Measures
Location		Variability
Mean	53.06000	Std Deviation	19.92930
Median	57.60000	Variance	397.17686
Mode	.	Range	54.10000
		Interquartile Range	39.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	10.31148	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	82.5
99%	82.5
95%	82.5
90%	79.8
75% Q3	68.6
50% Median	57.6
25% Q1	29.6
10%	29.2
5%	28.4
1%	28.4
0% Min	28.4

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
28.4	15	66.3	13
29.2	12	68.6	11
29.4	9	78.0	14
29.6	7	79.8	5
30.4	8	82.5	6

---

Univariate Analysis for Key Metrics

The UNIVARIATE Procedure

---

Univariate Analysis for Key Metrics

The UNIVARIATE Procedure

Fitted Normal Distribution for Performance_Score (Composite Performance Score)

Parameters for Normal Distribution
Parameter	Symbol	Estimate
Mean	Mu	53.06
Std Dev	Sigma	19.9293

Goodness-of-Fit Tests for Normal Distribution
Test	Statistic		p Value
Kolmogorov-Smirnov	D	0.20556828	Pr > D	0.087
Cramer-von Mises	W-Sq	0.08370795	Pr > W-Sq	0.176
Anderson-Darling	A-Sq	0.61317932	Pr > A-Sq	0.092

Quantiles for Normal Distribution
Percent	Quantile
	Observed	Estimated
1.0	28.4000	6.69752
5.0	28.4000	20.27922
10.0	29.2000	27.51958
25.0	29.6000	39.61789
50.0	57.6000	53.06000
75.0	68.6000	66.50211
90.0	79.8000	78.60042
95.0	82.5000	85.84078
99.0	82.5000	99.42248

---

Univariate Analysis for Key Metrics

The UNIVARIATE Procedure

Variable: Price_USD

Moments
N	15	Sum Weights	15
Mean	723	Sum Observations	10845
Std Deviation	355.483574	Variance	126368.571
Skewness	-0.0380933	Kurtosis	-1.3433761
Uncorrected SS	9610095	Corrected SS	1769160
Coeff Variation	49.1678526	Std Error Mean	91.7854641

Basic Statistical Measures
Location		Variability
Mean	723.0000	Std Deviation	355.48357
Median	749.0000	Variance	126369
Mode	999.0000	Range	1050
		Interquartile Range	700.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	7.877064	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	1299
99%	1299
95%	1299
90%	1199
75% Q3	999
50% Median	749
25% Q1	299
10%	279
5%	249
1%	249
0% Min	249

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
249	9	999	1
279	15	999	5
279	8	999	10
299	12	1199	4
399	7	1299	6

---

Univariate Analysis for Key Metrics

The UNIVARIATE Procedure

Variable: Battery_Life_hours

Moments
N	14	Sum Weights	14
Mean	46.0214286	Sum Observations	644.3
Std Deviation	32.8496149	Variance	1079.0972
Skewness	-0.4882833	Kurtosis	-1.8419884
Uncorrected SS	43679.87	Corrected SS	14028.2636
Coeff Variation	71.3789552	Std Error Mean	8.77942887

Basic Statistical Measures
Location		Variability
Mean	46.02143	Std Deviation	32.84961
Median	61.90000	Variance	1079
Mode	6.00000	Range	76.40000
		Interquartile Range	68.70000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	5.241962	Pr > |t|	0.0002
Sign	M	7	Pr >= |M|	0.0001
Signed Rank	S	52.5	Pr >= |S|	0.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	80.0
99%	80.0
95%	80.0
90%	77.3
75% Q3	74.7
50% Median	61.9
25% Q1	6.0
10%	3.7
5%	3.6
1%	3.6
0% Min	3.6

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
3.6	7	72.0	13
3.7	12	74.7	14
4.9	8	75.0	11
6.0	15	77.3	6
6.0	9	80.0	5

Missing Values
Missing Value	Count	Percent Of
		All Obs	Missing Obs
.	1	6.67	100.00

7) PROC SQL examples: Top 5 by default performance score and average price by category */

proc sql outobs=5;

  create table work.top5_default as

  select Model, Brand, Category, Performance_Score, Price_USD

  from work.gadgets_ranked_default

  order by Perf_Rank_Default;

  create table work.avg_price_by_cat as

  select Category, count(*) as Count, mean(Price_USD) as Avg_Price format=8.2

  from work.gadgets_score_default

  group by Category;

quit;

proc print data=work.top5_default;

run;

OUTPUT:

Obs	Model	Brand	Category	Performance_Score	Price_USD
1	ThinkPad X1 Carbon	Lenovo	Laptop	82.5	1299
2	Dell XPS 13	Dell	Laptop	79.8	999
3	Huawei MateBook 14	Huawei	Laptop	78.0	899
4	OnePlus 11	OnePlus	Phone	68.6	699
5	Oppo Find X6	Oppo	Phone	66.3	749

proc print data=work.avg_price_by_cat;

run;

OUTPUT:

Obs	Category	Count	Avg_Price
1	Earbuds	2	264.00
2	Laptop	4	1099.00
3	Phone	5	789.00
4	Smartwatch	3	325.67
5	Tablet	1	999.00

8) Visualizations using PROC SGPLOT 

proc sgplot data=work.gadgets_score_default;

  scatter x=Price_USD y=Performance_Score / datalabel=Model markerattrs=(symbol=CircleFilled);

  reg x=Price_USD y=Performance_Score; /* trend line */

  xaxis label='Price (USD)';

  yaxis label='Performance Score (0-100)';

  title 'Price vs Performance Score';

run;

OUTPUT:

proc sgplot data=work.gadgets_score_default;

  vbar Category / response=Performance_Score stat=mean datalabel;

  title 'Average Performance Score by Category';

run;

OUTPUT:

9) Demonstrate INTCK usage again — months since release for reporting 

proc sql;

  create table work.age_report as

  select Model, Brand, Category, ReleaseDate, 

         intck('month', ReleaseDate, today()) as Age_Months

  from work.gadgets_score_default

  order by Age_Months desc;

quit;

proc print data=work.age_report;

run;

OUTPUT:

Obs	Model	Brand	Category	ReleaseDate	Age_Months
1	MacBook Air M2	Apple	Laptop	2022-07-15	40
2	Galaxy Watch5	Samsung	Smartwatch	2022-08-26	39
3	ThinkPad X1 Carbon	Lenovo	Laptop	2022-09-05	38
4	iPhone 14 Pro	Apple	Phone	2022-09-16	38
5	AirPods Pro 2	Apple	Earbuds	2022-09-23	38
6	Pixel 7	Google	Phone	2022-10-13	37
7	Huawei MateBook 14	Huawei	Laptop	2022-11-25	36
8	Dell XPS 13	Dell	Laptop	2023-01-20	34
9	Galaxy S23	Samsung	Phone	2023-02-17	33
10	OnePlus 11	OnePlus	Phone	2023-02-07	33
11	Oppo Find X6	Oppo	Phone	2023-03-15	32
12	Bose QC Earbuds	Bose	Earbuds	2023-04-01	31
13	Pixel Watch 2	Google	Smartwatch	2023-05-10	30
14	Apple Watch Series 9	Apple	Smartwatch	2023-09-12	26
15	Surface Pro 9	Microsoft	Tablet	2023-10-03	25

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