Curve-Fit
ASO Keyword Dashboard
Tracking 70 keywords for Curve-Fit in Apple App Store
Curve-Fit tracks 70 keywords (5 keywords rank; 65 need traction). Key metrics: 0% top-10 coverage, opportunity 70.4, difficulty 38.5, best rank 11.
Tracked keywords
70
5 ranked • 65 not ranking yet
Top 10 coverage
0%
Best rank 11 • Latest leader 147
Avg opportunity
70.4
Top keyword: straight
Avg difficulty
38.5
Lower scores indicate easier wins
Opportunity leaders
- 67.4
straight
Opportunity: 73.0 • Difficulty: 45.5 • Rank 147
Competitors: 246
- 66.2
format
Opportunity: 73.0 • Difficulty: 41.0 • Rank —
Competitors: 99
- 66.7
needed
Opportunity: 73.0 • Difficulty: 41.4 • Rank —
Competitors: 115
- 67.5
functions
Opportunity: 73.0 • Difficulty: 41.6 • Rank —
Competitors: 108
- 67.5
done
Opportunity: 73.0 • Difficulty: 44.2 • Rank —
Competitors: 196
Unranked opportunities
format
Opportunity: 73.0 • Difficulty: 41.0 • Competitors: 99
needed
Opportunity: 73.0 • Difficulty: 41.4 • Competitors: 115
functions
Opportunity: 73.0 • Difficulty: 41.6 • Competitors: 108
done
Opportunity: 73.0 • Difficulty: 44.2 • Competitors: 196
routine
Opportunity: 73.0 • Difficulty: 40.1 • Competitors: 86
High competition keywords
free
Total apps: 175,988 • Major competitors: 3,927
Latest rank: — • Difficulty: 55.7
find
Total apps: 149,879 • Major competitors: 2,796
Latest rank: — • Difficulty: 55.0
best
Total apps: 124,558 • Major competitors: 2,643
Latest rank: — • Difficulty: 53.9
using
Total apps: 114,667 • Major competitors: 1,563
Latest rank: — • Difficulty: 53.1
data
Total apps: 97,664 • Major competitors: 1,018
Latest rank: — • Difficulty: 51.5
All tracked keywords
Includes opportunity, difficulty, rankings and competitor benchmarks
| Major Competitors | |||||||
|---|---|---|---|---|---|---|---|
| least | 70 | 100 | 49 | 74 30,249 competing apps Median installs: 1,300 Avg rating: 4.2 | 11 | 11 | 950 major competitor apps |
| curve | 71 | 100 | 40 | 55 1,970 competing apps Median installs: 450 Avg rating: 4.1 | 19 | 19 | 20 major competitor apps |
| fitting | 70 | 100 | 32 | 52 1,262 competing apps Median installs: 450 Avg rating: 4.0 | 52 | 52 | 16 major competitor apps |
| straight | 73 | 100 | 46 | 67 11,243 competing apps Median installs: 650 Avg rating: 4.1 | 147 | 147 | 246 major competitor apps |
| linear | 70 | 100 | 31 | 52 1,321 competing apps Median installs: 550 Avg rating: 4.1 | 151 | 151 | 9 major competitor apps |
| free | 65 | 100 | 56 | 87 175,988 competing apps Median installs: 750 Avg rating: 4.1 | — | — | 3,927 major competitor apps |
| best | 66 | 100 | 54 | 85 124,558 competing apps Median installs: 550 Avg rating: 4.1 | — | — | 2,643 major competitor apps |
| version | 70 | 100 | 46 | 75 30,986 competing apps Median installs: 500 Avg rating: 4.0 | — | — | 384 major competitor apps |
| used | 69 | 100 | 49 | 78 50,553 competing apps Median installs: 350 Avg rating: 4.0 | — | — | 504 major competitor apps |
| using | 66 | 100 | 53 | 84 114,667 competing apps Median installs: 450 Avg rating: 4.0 | — | — | 1,563 major competitor apps |
| find | 66 | 100 | 55 | 86 149,879 competing apps Median installs: 500 Avg rating: 4.1 | — | — | 2,796 major competitor apps |
| format | 73 | 100 | 41 | 66 9,589 competing apps Median installs: 495 Avg rating: 4.0 | — | — | 99 major competitor apps |
| technique | 72 | 100 | 35 | 58 3,086 competing apps Median installs: 400 Avg rating: 4.1 | — | — | 16 major competitor apps |
| purchase | 69 | 100 | 49 | 77 45,096 competing apps Median installs: 700 Avg rating: 4.2 | — | — | 881 major competitor apps |
| minimum | 72 | 100 | 41 | 60 4,116 competing apps Median installs: 500 Avg rating: 4.0 | — | — | 94 major competitor apps |
| many | 68 | 100 | 50 | 81 76,904 competing apps Median installs: 500 Avg rating: 4.1 | — | — | 1,359 major competitor apps |
| fit | 72 | 100 | 45 | 71 17,641 competing apps Median installs: 550 Avg rating: 4.2 | — | — | 342 major competitor apps |
| needed | 73 | 100 | 41 | 67 10,295 competing apps Median installs: 400 Avg rating: 4.1 | — | — | 115 major competitor apps |
| functions | 73 | 100 | 42 | 67 11,458 competing apps Median installs: 350 Avg rating: 3.8 | — | — | 108 major competitor apps |
| literature | 70 | 100 | 33 | 50 1,022 competing apps Median installs: 400 Avg rating: 4.1 | — | — | 8 major competitor apps |
| required | 72 | 100 | 44 | 71 18,719 competing apps Median installs: 400 Avg rating: 4.0 | — | — | 236 major competitor apps |
| done | 73 | 100 | 44 | 67 11,418 competing apps Median installs: 500 Avg rating: 4.1 | — | — | 196 major competitor apps |
| routine | 73 | 100 | 40 | 65 7,693 competing apps Median installs: 500 Avg rating: 4.2 | — | — | 86 major competitor apps |
| generated | 73 | 100 | 41 | 67 10,453 competing apps Median installs: 400 Avg rating: 4.1 | — | — | 80 major competitor apps |
| data | 67 | 100 | 52 | 83 97,664 competing apps Median installs: 400 Avg rating: 4.0 | — | — | 1,018 major competitor apps |
App Description
The curve-fitting technique used in this app is based on regression analysis by the method of least squares. The free version fits a straight line through a data-set using least squares analysis.
One In-App purchase is required to fit the other equations to the data set:
Straight Line : Y = C0 + C1*X (free)
Power Curve : Y = C0 + X^C1 Exponential I : Y = C0 * EXP(C1*X)
Exponential II : Y = C0 * X * EXP(C1*X)
Hyperbolic : Y = (C0 + C1*X)/(1 - C2*X)
Square Root : Y = C0 + C1*SQRT(X)
Polynomial : Y = C0 + C1*X + --- + CN*X^N
Exponential Poly : Y = C0 * EXP(C1*X + --- +
Natural Log : Y = C0 + C1*(LN(X)) + --- +
Reciprocal : Y = C0 + C1/X + --- + CN/X^N
Most literature deals with least squares analysis for straight lines, 2nd degree polynomials, and functions that can be linearized. The input-data is transformed into a format that the can be put into linear forms with undetermined constants. These types of equations are applicable for least-squares regression.
The regression routine is needed for determining values for the set of unknown quantities C1, C2,- - - ,Cm in the equation:
Y = C1 x F1(X) + C2 x F2(X) + - - - + Cm x Fm(X)
The constants are determined to minimize the sum of squares of the differences between the measured values (Y1, Y2, - - - , Yn) and the predicted equation Yc = F(X) which is found by curve-fitting the given data.
The principle of least squares is to find the values for the unknowns C1 through Cm that will minimize the sum of the squares of the residuals:
n
∑(ri) = r12 + r22 + - - - + rn2 = minimum
i=1
This is done by letting the derivative of the above equation equal zero. Thereby there will be generated as many algebraic equations as given data points, and the number of equations will be larger than unknowns.